Overview

Dataset statistics

Number of variables65
Number of observations2431
Missing cells23332
Missing cells (%)14.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory520.1 B

Variable types

Numeric9
Text4
Categorical51
DateTime1

Alerts

vote_id is highly overall correlated with vote_id2 and 7 other fieldsHigh correlation
vote_id2 is highly overall correlated with vote_id and 15 other fieldsHigh correlation
elecper is highly overall correlated with vote_id and 15 other fieldsHigh correlation
vote_type is highly overall correlated with vote_finalpassage and 3 other fieldsHigh correlation
policy2 is highly overall correlated with sponsor_kpd and 1 other fieldsHigh correlation
policy3 is highly overall correlated with sponsor3 and 9 other fieldsHigh correlation
cabid_parlgov is highly overall correlated with sponsor4 and 10 other fieldsHigh correlation
cabid_erdda is highly overall correlated with vote_id and 12 other fieldsHigh correlation
vote_finalpassage is highly overall correlated with vote_type and 2 other fieldsHigh correlation
vote_numproposals is highly overall correlated with vote_id and 7 other fieldsHigh correlation
sponsor1 is highly overall correlated with vote_numproposals and 21 other fieldsHigh correlation
sponsor2 is highly overall correlated with vote_id2 and 22 other fieldsHigh correlation
sponsor3 is highly overall correlated with vote_id2 and 39 other fieldsHigh correlation
sponsor4 is highly overall correlated with vote_id2 and 32 other fieldsHigh correlation
sponsor_kpd is highly overall correlated with policy2 and 10 other fieldsHigh correlation
sponsor_leftpds is highly overall correlated with sponsor1 and 6 other fieldsHigh correlation
sponsor_greens is highly overall correlated with sponsor1 and 3 other fieldsHigh correlation
sponsor_spd is highly overall correlated with sponsor1 and 5 other fieldsHigh correlation
sponsor_fdp is highly overall correlated with sponsor1 and 8 other fieldsHigh correlation
sponsor_cducsu is highly overall correlated with sponsor1 and 5 other fieldsHigh correlation
sponsor_dsu is highly overall correlated with policy2 and 7 other fieldsHigh correlation
sponsor_gbbhe is highly overall correlated with sponsor1 and 8 other fieldsHigh correlation
sponsor_dafvp is highly overall correlated with policy3 and 8 other fieldsHigh correlation
sponsor_dp is highly overall correlated with sponsor3 and 6 other fieldsHigh correlation
sponsor_fu is highly overall correlated with sponsor1 and 9 other fieldsHigh correlation
sponsor_noparty is highly overall correlated with sponsor1 and 6 other fieldsHigh correlation
sponsor_govall is highly overall correlated with vote_type and 6 other fieldsHigh correlation
sponsor_govone is highly overall correlated with vote_type and 6 other fieldsHigh correlation
sponsor_mps is highly overall correlated with sponsor1 and 1 other fieldsHigh correlation
sponsor_afd is highly overall correlated with vote_id2 and 28 other fieldsHigh correlation
request1 is highly overall correlated with sponsor_leftpds and 20 other fieldsHigh correlation
request2 is highly overall correlated with vote_id and 35 other fieldsHigh correlation
request3 is highly overall correlated with vote_id and 41 other fieldsHigh correlation
request4 is highly overall correlated with vote_id2 and 33 other fieldsHigh correlation
request_kpd is highly overall correlated with sponsor4 and 6 other fieldsHigh correlation
request_leftpds is highly overall correlated with sponsor1 and 5 other fieldsHigh correlation
request_greens is highly overall correlated with sponsor3 and 4 other fieldsHigh correlation
request_spd is highly overall correlated with sponsor1 and 5 other fieldsHigh correlation
request_fdp is highly overall correlated with sponsor4 and 6 other fieldsHigh correlation
request_cducsu is highly overall correlated with request1 and 5 other fieldsHigh correlation
request_gbbhe is highly overall correlated with policy3 and 9 other fieldsHigh correlation
request_dafvp is highly overall correlated with policy3 and 8 other fieldsHigh correlation
request_dp is highly overall correlated with sponsor3 and 7 other fieldsHigh correlation
request_fu is highly overall correlated with sponsor1 and 9 other fieldsHigh correlation
request_afd is highly overall correlated with vote_id2 and 28 other fieldsHigh correlation
request_noparty is highly overall correlated with vote_id and 7 other fieldsHigh correlation
request_unknown is highly overall correlated with sponsor3 and 7 other fieldsHigh correlation
request_gov is highly overall correlated with sponsor3 and 6 other fieldsHigh correlation
request_govpart is highly overall correlated with request1 and 6 other fieldsHigh correlation
request_oppo is highly overall correlated with sponsor4 and 7 other fieldsHigh correlation
request_govoppo is highly overall correlated with sponsor3 and 9 other fieldsHigh correlation
free_vote is highly overall correlated with sponsor1High correlation
bundesrat is highly overall correlated with elecper and 9 other fieldsHigh correlation
cabinet is highly overall correlated with vote_id2 and 17 other fieldsHigh correlation
cab_start is highly overall correlated with vote_id2 and 17 other fieldsHigh correlation
cab_end is highly overall correlated with vote_id2 and 17 other fieldsHigh correlation
elecper_start is highly overall correlated with vote_id and 22 other fieldsHigh correlation
elecper_end is highly overall correlated with vote_id2 and 18 other fieldsHigh correlation
cab_parties is highly overall correlated with vote_id2 and 17 other fieldsHigh correlation
vote_numproposals is highly imbalanced (90.9%)Imbalance
sponsor4 is highly imbalanced (72.6%)Imbalance
sponsor_kpd is highly imbalanced (99.5%)Imbalance
sponsor_leftpds is highly imbalanced (63.9%)Imbalance
sponsor_dsu is highly imbalanced (99.0%)Imbalance
sponsor_gbbhe is highly imbalanced (90.9%)Imbalance
sponsor_dafvp is highly imbalanced (98.2%)Imbalance
sponsor_dp is highly imbalanced (78.9%)Imbalance
sponsor_fu is highly imbalanced (96.8%)Imbalance
sponsor_mps is highly imbalanced (65.8%)Imbalance
sponsor_afd is highly imbalanced (59.1%)Imbalance
request4 is highly imbalanced (87.1%)Imbalance
request_kpd is highly imbalanced (99.5%)Imbalance
request_leftpds is highly imbalanced (70.6%)Imbalance
request_fdp is highly imbalanced (61.6%)Imbalance
request_gbbhe is highly imbalanced (97.2%)Imbalance
request_dafvp is highly imbalanced (98.2%)Imbalance
request_dp is highly imbalanced (94.6%)Imbalance
request_fu is highly imbalanced (97.9%)Imbalance
request_noparty is highly imbalanced (93.7%)Imbalance
free_vote is highly imbalanced (69.1%)Imbalance
policy2 has 1305 (53.7%) missing valuesMissing
policy3 has 2216 (91.2%) missing valuesMissing
sponsor2 has 1614 (66.4%) missing valuesMissing
sponsor3 has 2314 (95.2%) missing valuesMissing
sponsor4 has 2188 (90.0%) missing valuesMissing
sponsor_afd has 2187 (90.0%) missing valuesMissing
request2 has 2208 (90.8%) missing valuesMissing
request3 has 2404 (98.9%) missing valuesMissing
request4 has 2209 (90.9%) missing valuesMissing
request_afd has 2187 (90.0%) missing valuesMissing
request_govoppo has 86 (3.5%) missing valuesMissing
gesta has 1356 (55.8%) missing valuesMissing
cabid_erdda has 460 (18.9%) missing valuesMissing
elecper_start has 211 (8.7%) missing valuesMissing
elecper_end has 385 (15.8%) missing valuesMissing
vote_id has unique valuesUnique

Reproduction

Analysis started2023-12-03 10:22:18.251272
Analysis finished2023-12-03 10:22:38.532985
Duration20.28 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

vote_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct2431
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13609.729
Minimum1001
Maximum181154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:38.612987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1122.5
Q110027.5
median13161
Q317151.5
95-th percentile19150.5
Maximum181154
Range180153
Interquartile range (IQR)7124

Descriptive statistics

Standard deviation13688.297
Coefficient of variation (CV)1.0057729
Kurtosis75.197646
Mean13609.729
Median Absolute Deviation (MAD)3964
Skewness7.7217017
Sum33085251
Variance1.8736946 × 108
MonotonicityNot monotonic
2023-12-03T11:22:38.746278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001 1
 
< 0.1%
16097 1
 
< 0.1%
16099 1
 
< 0.1%
16100 1
 
< 0.1%
16101 1
 
< 0.1%
16102 1
 
< 0.1%
16103 1
 
< 0.1%
16104 1
 
< 0.1%
16105 1
 
< 0.1%
16106 1
 
< 0.1%
Other values (2421) 2421
99.6%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
ValueCountFrequency (%)
181154 1
< 0.1%
181153 1
< 0.1%
181152 1
< 0.1%
181151 1
< 0.1%
141642 1
< 0.1%
141641 1
< 0.1%
141412 1
< 0.1%
141411 1
< 0.1%
141403 1
< 0.1%
141402 1
< 0.1%

vote_id2
Real number (ℝ)

HIGH CORRELATION 

Distinct2415
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12368.497
Minimum1001
Maximum120092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:38.882092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1122.5
Q110021.5
median13145
Q317128.5
95-th percentile19123.5
Maximum120092
Range119091
Interquartile range (IQR)7107

Descriptive statistics

Standard deviation6025.0077
Coefficient of variation (CV)0.48712531
Kurtosis40.94536
Mean12368.497
Median Absolute Deviation (MAD)3963
Skewness1.7655326
Sum30067815
Variance36300717
MonotonicityNot monotonic
2023-12-03T11:22:39.013310image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7012 4
 
0.2%
18115 4
 
0.2%
13128 3
 
0.1%
14140 3
 
0.1%
14141 2
 
0.1%
14164 2
 
0.1%
7013 2
 
0.1%
13127 2
 
0.1%
11014 2
 
0.1%
11013 2
 
0.1%
Other values (2405) 2405
98.9%
ValueCountFrequency (%)
1001 1
< 0.1%
1002 1
< 0.1%
1003 1
< 0.1%
1004 1
< 0.1%
1005 1
< 0.1%
1006 1
< 0.1%
1007 1
< 0.1%
1008 1
< 0.1%
1009 1
< 0.1%
1010 1
< 0.1%
ValueCountFrequency (%)
120092 1
< 0.1%
19244 1
< 0.1%
19243 1
< 0.1%
19242 1
< 0.1%
19241 1
< 0.1%
19240 1
< 0.1%
19239 1
< 0.1%
19238 1
< 0.1%
19237 1
< 0.1%
19236 1
< 0.1%
Distinct301
Distinct (%)12.4%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:39.271045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3393665
Min length1

Characters and Unicode

Total characters5687
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)2.3%

Sample

1st row1
2nd row2
3rd row3
4th row4
5th row5
ValueCountFrequency (%)
1 19
 
0.8%
3 19
 
0.8%
15 19
 
0.8%
4 19
 
0.8%
5 19
 
0.8%
6 19
 
0.8%
7 19
 
0.8%
8 19
 
0.8%
10 19
 
0.8%
11 19
 
0.8%
Other values (291) 2241
92.2%
2023-12-03T11:22:39.654075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1394
24.5%
2 719
12.6%
3 526
 
9.2%
4 493
 
8.7%
5 467
 
8.2%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.0%
8 397
 
7.0%
9 395
 
6.9%
Other values (4) 28
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5659
99.5%
Lowercase Letter 28
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1394
24.6%
2 719
12.7%
3 526
 
9.3%
4 493
 
8.7%
5 467
 
8.3%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.1%
8 397
 
7.0%
9 395
 
7.0%
Lowercase Letter
ValueCountFrequency (%)
b 11
39.3%
a 11
39.3%
c 4
 
14.3%
d 2
 
7.1%

Most occurring scripts

ValueCountFrequency (%)
Common 5659
99.5%
Latin 28
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1394
24.6%
2 719
12.7%
3 526
 
9.3%
4 493
 
8.7%
5 467
 
8.3%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.1%
8 397
 
7.0%
9 395
 
7.0%
Latin
ValueCountFrequency (%)
b 11
39.3%
a 11
39.3%
c 4
 
14.3%
d 2
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1394
24.5%
2 719
12.6%
3 526
 
9.2%
4 493
 
8.7%
5 467
 
8.2%
6 447
 
7.9%
7 421
 
7.4%
0 400
 
7.0%
8 397
 
7.0%
9 395
 
6.9%
Other values (4) 28
 
0.5%

elecper
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.236528
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:39.778601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q110
median13
Q317
95-th percentile19
Maximum19
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.5928745
Coefficient of variation (CV)0.45706384
Kurtosis-0.64050416
Mean12.236528
Median Absolute Deviation (MAD)4
Skewness-0.70914434
Sum29747
Variance31.280245
MonotonicityIncreasing
2023-12-03T11:22:39.874364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
17 275
11.3%
19 244
10.0%
11 218
9.0%
18 216
8.9%
13 180
 
7.4%
16 177
 
7.3%
2 169
 
7.0%
14 168
 
6.9%
10 134
 
5.5%
1 133
 
5.5%
Other values (9) 517
21.3%
ValueCountFrequency (%)
1 133
5.5%
2 169
7.0%
3 46
 
1.9%
4 37
 
1.5%
5 24
 
1.0%
6 38
 
1.6%
7 55
 
2.3%
8 59
 
2.4%
9 26
 
1.1%
10 134
5.5%
ValueCountFrequency (%)
19 244
10.0%
18 216
8.9%
17 275
11.3%
16 177
7.3%
15 102
 
4.2%
14 168
6.9%
13 180
7.4%
12 130
5.3%
11 218
9.0%
10 134
5.5%

source
Text

Distinct2223
Distinct (%)91.4%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:40.045794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length14
Median length12
Mean length11.445907
Min length7

Characters and Unicode

Total characters27825
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2138 ?
Unique (%)87.9%

Sample

1st row01/069/2520
2nd row01/076/2738
3rd row01/079/2923
4th row01/150/5989
5th row01/183/7787
ValueCountFrequency (%)
01/242/11504 23
 
0.9%
01/223/10030 11
 
0.5%
02/187/10620 9
 
0.4%
02/071/3859 7
 
0.3%
02/184/10284 7
 
0.3%
01/208/9113 7
 
0.3%
01/280/14224 6
 
0.2%
02/058/2995 6
 
0.2%
01/212/9339 6
 
0.2%
02/073/4044 5
 
0.2%
Other values (2213) 2344
96.4%
2023-12-03T11:22:40.354656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 5588
20.1%
/ 4862
17.5%
0 2972
10.7%
2 2790
10.0%
3 1932
 
6.9%
4 1802
 
6.5%
7 1734
 
6.2%
8 1651
 
5.9%
9 1612
 
5.8%
6 1547
 
5.6%
Other values (2) 1335
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22937
82.4%
Other Punctuation 4862
 
17.5%
Space Separator 26
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5588
24.4%
0 2972
13.0%
2 2790
12.2%
3 1932
 
8.4%
4 1802
 
7.9%
7 1734
 
7.6%
8 1651
 
7.2%
9 1612
 
7.0%
6 1547
 
6.7%
5 1309
 
5.7%
Other Punctuation
ValueCountFrequency (%)
/ 4862
100.0%
Space Separator
ValueCountFrequency (%)
26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27825
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5588
20.1%
/ 4862
17.5%
0 2972
10.7%
2 2790
10.0%
3 1932
 
6.9%
4 1802
 
6.5%
7 1734
 
6.2%
8 1651
 
5.9%
9 1612
 
5.8%
6 1547
 
5.6%
Other values (2) 1335
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27825
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5588
20.1%
/ 4862
17.5%
0 2972
10.7%
2 2790
10.0%
3 1932
 
6.9%
4 1802
 
6.5%
7 1734
 
6.2%
8 1651
 
5.9%
9 1612
 
5.8%
6 1547
 
5.6%
Other values (2) 1335
 
4.8%
Distinct2417
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:40.546309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length1269
Median length457
Mean length224.50062
Min length31

Characters and Unicode

Total characters545761
Distinct characters77
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2409 ?
Unique (%)99.1%

Sample

1st rowEntwurf eines Gesetzes ��ber den Beitritt der Bundesrepublik Deutschland zum Europarat (Drucksache Nr. 984)
2nd rowHandschriftlicher ��nderungsantrag der Abgeordneten Pelster und Genossen zu ��1 Abs. 1 des Entwurfs eines Richterwahlgesetzes (Drucksache Nr. 1088)
3rd rowArtikel I Ziffer 2 des Entwurfs eines Gesetzes zur ��nderung des Umsatzsteuergesetzes (Drucksachen Nr. 1123 und 1215)
4th rowAntrag der Fraktion der Deutschen Partei betreffend Einsetzung eines Untersuchungsausschusses (Drucksachen Nr. 2234)
5th rowArtikel I des Entwurfs eines Gesetzes betreffend den Vertrag ��ber die Gr��ndung der Europ��ischen Gemeinschaft f��r Kohle und Stahl (Drucksachen Nr. 2401)
ValueCountFrequency (%)
der 5808
 
8.6%
des 3026
 
4.5%
und 2753
 
4.1%
zur 1584
 
2.4%
fraktion 1244
 
1.8%
eines 1070
 
1.6%
987
 
1.5%
gesetzes 965
 
1.4%
drs 913
 
1.4%
��ber 821
 
1.2%
Other values (8112) 48178
71.5%
2023-12-03T11:22:40.894366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
65083
 
11.9%
e 60552
 
11.1%
n 37157
 
6.8%
r 36966
 
6.8%
s 32743
 
6.0%
t 25158
 
4.6%
u 24683
 
4.5%
d 20320
 
3.7%
i 19108
 
3.5%
a 17297
 
3.2%
Other values (67) 206694
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 365126
66.9%
Space Separator 65083
 
11.9%
Uppercase Letter 43588
 
8.0%
Decimal Number 35418
 
6.5%
Other Symbol 15490
 
2.8%
Other Punctuation 12488
 
2.3%
Close Punctuation 3403
 
0.6%
Open Punctuation 3403
 
0.6%
Dash Punctuation 1762
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 60552
16.6%
n 37157
10.2%
r 36966
10.1%
s 32743
9.0%
t 25158
 
6.9%
u 24683
 
6.8%
d 20320
 
5.6%
i 19108
 
5.2%
a 17297
 
4.7%
g 15582
 
4.3%
Other values (16) 75560
20.7%
Uppercase Letter
ValueCountFrequency (%)
D 5620
12.9%
A 4245
9.7%
B 4059
9.3%
E 3767
 
8.6%
G 3674
 
8.4%
S 3523
 
8.1%
F 3102
 
7.1%
N 2358
 
5.4%
U 1683
 
3.9%
I 1486
 
3.4%
Other values (16) 10071
23.1%
Decimal Number
ValueCountFrequency (%)
1 8777
24.8%
2 3956
11.2%
0 3902
11.0%
9 3479
 
9.8%
3 3109
 
8.8%
4 2636
 
7.4%
7 2607
 
7.4%
8 2521
 
7.1%
5 2242
 
6.3%
6 2189
 
6.2%
Other Punctuation
ValueCountFrequency (%)
/ 4746
38.0%
, 3675
29.4%
. 3425
27.4%
: 401
 
3.2%
; 139
 
1.1%
\ 51
 
0.4%
" 48
 
0.4%
? 3
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 3395
99.8%
] 8
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 3394
99.7%
[ 9
 
0.3%
Space Separator
ValueCountFrequency (%)
65083
100.0%
Other Symbol
ValueCountFrequency (%)
� 15490
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 408714
74.9%
Common 137047
 
25.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 60552
14.8%
n 37157
 
9.1%
r 36966
 
9.0%
s 32743
 
8.0%
t 25158
 
6.2%
u 24683
 
6.0%
d 20320
 
5.0%
i 19108
 
4.7%
a 17297
 
4.2%
g 15582
 
3.8%
Other values (42) 119148
29.2%
Common
ValueCountFrequency (%)
65083
47.5%
� 15490
 
11.3%
1 8777
 
6.4%
/ 4746
 
3.5%
2 3956
 
2.9%
0 3902
 
2.8%
, 3675
 
2.7%
9 3479
 
2.5%
. 3425
 
2.5%
) 3395
 
2.5%
Other values (15) 21119
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 530271
97.2%
Specials 15490
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
65083
 
12.3%
e 60552
 
11.4%
n 37157
 
7.0%
r 36966
 
7.0%
s 32743
 
6.2%
t 25158
 
4.7%
u 24683
 
4.7%
d 20320
 
3.8%
i 19108
 
3.6%
a 17297
 
3.3%
Other values (66) 191204
36.1%
Specials
ValueCountFrequency (%)
� 15490
100.0%

vote_type
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.5%
Missing2
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean7.5343763
Minimum1
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:41.012908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median8
Q310
95-th percentile10
Maximum88
Range87
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.7844666
Coefficient of variation (CV)0.90046824
Kurtosis111.47719
Mean7.5343763
Median Absolute Deviation (MAD)2
Skewness9.5307285
Sum18301
Variance46.028987
MonotonicityNot monotonic
2023-12-03T11:22:41.105353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
10 728
29.9%
5 635
26.1%
8 408
16.8%
1 255
 
10.5%
9 250
 
10.3%
7 75
 
3.1%
3 19
 
0.8%
6 14
 
0.6%
88 14
 
0.6%
2 13
 
0.5%
Other values (3) 18
 
0.7%
ValueCountFrequency (%)
1 255
 
10.5%
2 13
 
0.5%
3 19
 
0.8%
4 7
 
0.3%
5 635
26.1%
6 14
 
0.6%
7 75
 
3.1%
8 408
16.8%
9 250
 
10.3%
10 728
29.9%
ValueCountFrequency (%)
88 14
 
0.6%
12 4
 
0.2%
11 7
 
0.3%
10 728
29.9%
9 250
 
10.3%
8 408
16.8%
7 75
 
3.1%
6 14
 
0.6%
5 635
26.1%
4 7
 
0.3%

vote_finalpassage
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0
1970 
1
461 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1970
81.0%
1 461
 
19.0%

Length

2023-12-03T11:22:41.212361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:41.328362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 1970
81.0%
1 461
 
19.0%

Most occurring characters

ValueCountFrequency (%)
0 1970
81.0%
1 461
 
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1970
81.0%
1 461
 
19.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1970
81.0%
1 461
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1970
81.0%
1 461
 
19.0%

vote_numproposals
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0
2403 
1
 
28

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2403
98.8%
1 28
 
1.2%

Length

2023-12-03T11:22:41.413360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:41.510359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2403
98.8%
1 28
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 2403
98.8%
1 28
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2403
98.8%
1 28
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2403
98.8%
1 28
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2403
98.8%
1 28
 
1.2%

policy1
Real number (ℝ)

Distinct23
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.373097
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:41.610738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median12
Q316
95-th percentile26
Maximum99
Range98
Interquartile range (IQR)14

Descriptive statistics

Standard deviation11.822184
Coefficient of variation (CV)1.0394867
Kurtosis27.268873
Mean11.373097
Median Absolute Deviation (MAD)7
Skewness4.0124167
Sum27648
Variance139.76403
MonotonicityNot monotonic
2023-12-03T11:22:41.722739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 532
21.9%
16 337
13.9%
13 256
10.5%
3 147
 
6.0%
19 138
 
5.7%
20 130
 
5.3%
2 125
 
5.1%
12 123
 
5.1%
5 105
 
4.3%
27 98
 
4.0%
Other values (13) 440
18.1%
ValueCountFrequency (%)
1 532
21.9%
2 125
 
5.1%
3 147
 
6.0%
4 35
 
1.4%
5 105
 
4.3%
6 28
 
1.2%
7 53
 
2.2%
8 69
 
2.8%
10 34
 
1.4%
12 123
 
5.1%
ValueCountFrequency (%)
99 24
 
1.0%
27 98
 
4.0%
25 84
 
3.5%
24 1
 
< 0.1%
21 7
 
0.3%
20 130
 
5.3%
19 138
5.7%
18 28
 
1.2%
17 1
 
< 0.1%
16 337
13.9%

policy2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)2.0%
Missing1305
Missing (%)53.7%
Infinite0
Infinite (%)0.0%
Mean13.804618
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:41.839737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q17
median16
Q319
95-th percentile24
Maximum27
Range26
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3244198
Coefficient of variation (CV)0.5305775
Kurtosis-1.0482631
Mean13.804618
Median Absolute Deviation (MAD)4
Skewness-0.48673161
Sum15544
Variance53.647125
MonotonicityNot monotonic
2023-12-03T11:22:41.958739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
19 309
 
12.7%
20 141
 
5.8%
1 95
 
3.9%
13 84
 
3.5%
5 68
 
2.8%
12 62
 
2.6%
2 59
 
2.4%
16 56
 
2.3%
25 38
 
1.6%
7 38
 
1.6%
Other values (12) 176
 
7.2%
(Missing) 1305
53.7%
ValueCountFrequency (%)
1 95
3.9%
2 59
2.4%
3 16
 
0.7%
4 17
 
0.7%
5 68
2.8%
6 11
 
0.5%
7 38
 
1.6%
8 23
 
0.9%
10 19
 
0.8%
12 62
2.6%
ValueCountFrequency (%)
27 11
 
0.5%
25 38
 
1.6%
24 28
 
1.2%
21 1
 
< 0.1%
20 141
5.8%
19 309
12.7%
18 12
 
0.5%
17 15
 
0.6%
16 56
 
2.3%
15 13
 
0.5%

policy3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)8.8%
Missing2216
Missing (%)91.2%
Infinite0
Infinite (%)0.0%
Mean13.786047
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:42.067768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median15
Q320
95-th percentile25
Maximum27
Range26
Interquartile range (IQR)15

Descriptive statistics

Standard deviation7.9072184
Coefficient of variation (CV)0.57356679
Kurtosis-1.2048631
Mean13.786047
Median Absolute Deviation (MAD)5
Skewness-0.27144515
Sum2964
Variance62.524103
MonotonicityNot monotonic
2023-12-03T11:22:42.177511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20 33
 
1.4%
19 23
 
0.9%
13 18
 
0.7%
3 16
 
0.7%
25 14
 
0.6%
14 14
 
0.6%
5 13
 
0.5%
1 13
 
0.5%
2 12
 
0.5%
17 10
 
0.4%
Other values (9) 49
 
2.0%
(Missing) 2216
91.2%
ValueCountFrequency (%)
1 13
0.5%
2 12
0.5%
3 16
0.7%
4 5
 
0.2%
5 13
0.5%
7 2
 
0.1%
8 9
0.4%
12 3
 
0.1%
13 18
0.7%
14 14
0.6%
ValueCountFrequency (%)
27 6
 
0.2%
25 14
0.6%
24 8
 
0.3%
20 33
1.4%
19 23
0.9%
18 9
 
0.4%
17 10
 
0.4%
16 4
 
0.2%
15 3
 
0.1%
14 14
0.6%

sponsor1
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
CDU/CSU
685 
SPD
642 
Committee
409 
Greens
319 
Left/PDS
160 
Other values (13)
216 

Length

Max length43
Median length30
Mean length6.8165364
Min length2

Characters and Unicode

Total characters16571
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCDU/CSU
2nd rowCDU/CSU
3rd rowSPD
4th rowDP
5th rowCDU/CSU

Common Values

ValueCountFrequency (%)
CDU/CSU 685
28.2%
SPD 642
26.4%
Committee 409
16.8%
Greens 319
13.1%
Left/PDS 160
 
6.6%
FDP 105
 
4.3%
several parties (government and opposition) 50
 
2.1%
AfD 20
 
0.8%
GB/BHE 8
 
0.3%
FU 7
 
0.3%
Other values (8) 26
 
1.1%

Length

2023-12-03T11:22:42.306510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cdu/csu 685
25.8%
spd 642
24.2%
committee 409
15.4%
greens 319
12.0%
left/pds 160
 
6.0%
fdp 105
 
4.0%
several 50
 
1.9%
parties 50
 
1.9%
government 50
 
1.9%
and 50
 
1.9%
Other values (19) 137
 
5.2%

Most occurring characters

ValueCountFrequency (%)
e 1915
11.6%
C 1785
10.8%
D 1618
9.8%
S 1487
 
9.0%
U 1377
 
8.3%
t 1151
 
6.9%
P 917
 
5.5%
m 875
 
5.3%
/ 853
 
5.1%
o 621
 
3.7%
Other values (27) 3972
24.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7859
47.4%
Lowercase Letter 7533
45.5%
Other Punctuation 853
 
5.1%
Space Separator 226
 
1.4%
Open Punctuation 50
 
0.3%
Close Punctuation 50
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1915
25.4%
t 1151
15.3%
m 875
11.6%
o 621
 
8.2%
i 578
 
7.7%
n 555
 
7.4%
s 496
 
6.6%
r 496
 
6.6%
f 186
 
2.5%
a 172
 
2.3%
Other values (9) 488
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
C 1785
22.7%
D 1618
20.6%
S 1487
18.9%
U 1377
17.5%
P 917
11.7%
G 327
 
4.2%
L 160
 
2.0%
F 119
 
1.5%
B 29
 
0.4%
A 20
 
0.3%
Other values (4) 20
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 853
100.0%
Space Separator
ValueCountFrequency (%)
226
100.0%
Open Punctuation
ValueCountFrequency (%)
( 50
100.0%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 15392
92.9%
Common 1179
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1915
12.4%
C 1785
11.6%
D 1618
10.5%
S 1487
9.7%
U 1377
8.9%
t 1151
 
7.5%
P 917
 
6.0%
m 875
 
5.7%
o 621
 
4.0%
i 578
 
3.8%
Other values (23) 3068
19.9%
Common
ValueCountFrequency (%)
/ 853
72.3%
226
 
19.2%
( 50
 
4.2%
) 50
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16571
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1915
11.6%
C 1785
10.8%
D 1618
9.8%
S 1487
 
9.0%
U 1377
 
8.3%
t 1151
 
6.9%
P 917
 
5.5%
m 875
 
5.3%
/ 853
 
5.1%
o 621
 
3.7%
Other values (27) 3972
24.0%

sponsor2
Categorical

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)1.0%
Missing1614
Missing (%)66.4%
Memory size19.1 KiB
FDP
452 
SPD
194 
Greens
135 
DP
 
11
CDU/CSU
 
9
Other values (3)
 
16

Length

Max length8
Median length3
Mean length3.5948592
Min length2

Characters and Unicode

Total characters2937
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowFDP
2nd rowFDP
3rd rowFDP
4th rowFDP
5th rowFDP

Common Values

ValueCountFrequency (%)
FDP 452
 
18.6%
SPD 194
 
8.0%
Greens 135
 
5.6%
DP 11
 
0.5%
CDU/CSU 9
 
0.4%
GB/BHE 9
 
0.4%
Left/PDS 6
 
0.2%
FU 1
 
< 0.1%
(Missing) 1614
66.4%

Length

2023-12-03T11:22:42.430510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:42.585510image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fdp 452
55.3%
spd 194
23.7%
greens 135
 
16.5%
dp 11
 
1.3%
cdu/csu 9
 
1.1%
gb/bhe 9
 
1.1%
left/pds 6
 
0.7%
fu 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
D 672
22.9%
P 663
22.6%
F 453
15.4%
e 276
9.4%
S 209
 
7.1%
G 144
 
4.9%
r 135
 
4.6%
n 135
 
4.6%
s 135
 
4.6%
/ 24
 
0.8%
Other values (8) 91
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2220
75.6%
Lowercase Letter 693
 
23.6%
Other Punctuation 24
 
0.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 672
30.3%
P 663
29.9%
F 453
20.4%
S 209
 
9.4%
G 144
 
6.5%
U 19
 
0.9%
B 18
 
0.8%
C 18
 
0.8%
H 9
 
0.4%
E 9
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 276
39.8%
r 135
19.5%
n 135
19.5%
s 135
19.5%
f 6
 
0.9%
t 6
 
0.9%
Other Punctuation
ValueCountFrequency (%)
/ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2913
99.2%
Common 24
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 672
23.1%
P 663
22.8%
F 453
15.6%
e 276
9.5%
S 209
 
7.2%
G 144
 
4.9%
r 135
 
4.6%
n 135
 
4.6%
s 135
 
4.6%
U 19
 
0.7%
Other values (7) 72
 
2.5%
Common
ValueCountFrequency (%)
/ 24
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 672
22.9%
P 663
22.6%
F 453
15.4%
e 276
9.4%
S 209
 
7.1%
G 144
 
4.9%
r 135
 
4.6%
n 135
 
4.6%
s 135
 
4.6%
/ 24
 
0.8%
Other values (8) 91
 
3.1%

sponsor3
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)7.7%
Missing2314
Missing (%)95.2%
Memory size19.1 KiB
DP
51 
SPD
24 
FDP
11 
GB/BHE
11 
Greens
11 
Other values (4)

Length

Max length8
Median length7
Mean length3.2735043
Min length2

Characters and Unicode

Total characters383
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st rowDP
2nd rowDP
3rd rowDP
4th rowDP
5th rowDP

Common Values

ValueCountFrequency (%)
DP 51
 
2.1%
SPD 24
 
1.0%
FDP 11
 
0.5%
GB/BHE 11
 
0.5%
Greens 11
 
0.5%
FVP 3
 
0.1%
CDU/CSU 3
 
0.1%
DSU 2
 
0.1%
Left/PDS 1
 
< 0.1%
(Missing) 2314
95.2%

Length

2023-12-03T11:22:42.744540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:42.877877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
dp 51
43.6%
spd 24
20.5%
fdp 11
 
9.4%
gb/bhe 11
 
9.4%
greens 11
 
9.4%
fvp 3
 
2.6%
cdu/csu 3
 
2.6%
dsu 2
 
1.7%
left/pds 1
 
0.9%

Most occurring characters

ValueCountFrequency (%)
D 92
24.0%
P 90
23.5%
S 30
 
7.8%
e 23
 
6.0%
G 22
 
5.7%
B 22
 
5.7%
/ 15
 
3.9%
F 14
 
3.7%
s 11
 
2.9%
n 11
 
2.9%
Other values (9) 53
13.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 310
80.9%
Lowercase Letter 58
 
15.1%
Other Punctuation 15
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 92
29.7%
P 90
29.0%
S 30
 
9.7%
G 22
 
7.1%
B 22
 
7.1%
F 14
 
4.5%
E 11
 
3.5%
H 11
 
3.5%
U 8
 
2.6%
C 6
 
1.9%
Other values (2) 4
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
e 23
39.7%
s 11
19.0%
n 11
19.0%
r 11
19.0%
f 1
 
1.7%
t 1
 
1.7%
Other Punctuation
ValueCountFrequency (%)
/ 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 368
96.1%
Common 15
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 92
25.0%
P 90
24.5%
S 30
 
8.2%
e 23
 
6.2%
G 22
 
6.0%
B 22
 
6.0%
F 14
 
3.8%
s 11
 
3.0%
n 11
 
3.0%
r 11
 
3.0%
Other values (8) 42
11.4%
Common
ValueCountFrequency (%)
/ 15
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 383
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 92
24.0%
P 90
23.5%
S 30
 
7.8%
e 23
 
6.0%
G 22
 
5.7%
B 22
 
5.7%
/ 15
 
3.9%
F 14
 
3.7%
s 11
 
2.9%
n 11
 
2.9%
Other values (9) 53
13.8%

sponsor4
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct6
Distinct (%)2.5%
Missing2188
Missing (%)90.0%
Memory size19.1 KiB
.
216 
DP
 
14
FDP
 
6
Greens
 
4
SPD
 
2

Length

Max length6
Median length1
Mean length1.2139918
Min length1

Characters and Unicode

Total characters295
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowDP
2nd rowDP
3rd rowDP
4th rowDP
5th rowDP

Common Values

ValueCountFrequency (%)
. 216
 
8.9%
DP 14
 
0.6%
FDP 6
 
0.2%
Greens 4
 
0.2%
SPD 2
 
0.1%
FVP 1
 
< 0.1%
(Missing) 2188
90.0%

Length

2023-12-03T11:22:43.007298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:43.125593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
216
88.9%
dp 14
 
5.8%
fdp 6
 
2.5%
greens 4
 
1.6%
spd 2
 
0.8%
fvp 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
. 216
73.2%
P 23
 
7.8%
D 22
 
7.5%
e 8
 
2.7%
F 7
 
2.4%
G 4
 
1.4%
r 4
 
1.4%
n 4
 
1.4%
s 4
 
1.4%
S 2
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 216
73.2%
Uppercase Letter 59
 
20.0%
Lowercase Letter 20
 
6.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 23
39.0%
D 22
37.3%
F 7
 
11.9%
G 4
 
6.8%
S 2
 
3.4%
V 1
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
e 8
40.0%
r 4
20.0%
n 4
20.0%
s 4
20.0%
Other Punctuation
ValueCountFrequency (%)
. 216
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 216
73.2%
Latin 79
 
26.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 23
29.1%
D 22
27.8%
e 8
 
10.1%
F 7
 
8.9%
G 4
 
5.1%
r 4
 
5.1%
n 4
 
5.1%
s 4
 
5.1%
S 2
 
2.5%
V 1
 
1.3%
Common
ValueCountFrequency (%)
. 216
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 295
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 216
73.2%
P 23
 
7.8%
D 22
 
7.5%
e 8
 
2.7%
F 7
 
2.4%
G 4
 
1.4%
r 4
 
1.4%
n 4
 
1.4%
s 4
 
1.4%
S 2
 
0.7%

sponsor_kpd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2430 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2430
> 99.9%
1.0 1
 
< 0.1%

Length

2023-12-03T11:22:43.223562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:43.328563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2430
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4861
66.7%
. 2431
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4861
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4861
66.7%
. 2431
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4861
66.7%
. 2431
33.3%
1 1
 
< 0.1%

sponsor_leftpds
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2264 
1.0
 
167

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2264
93.1%
1.0 167
 
6.9%

Length

2023-12-03T11:22:43.412651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:43.508493image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2264
93.1%
1.0 167
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 4695
64.4%
. 2431
33.3%
1 167
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4695
96.6%
1 167
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4695
64.4%
. 2431
33.3%
1 167
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4695
64.4%
. 2431
33.3%
1 167
 
2.3%

sponsor_greens
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1962 
1.0
469 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1962
80.7%
1.0 469
 
19.3%

Length

2023-12-03T11:22:43.590429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:43.684711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1962
80.7%
1.0 469
 
19.3%

Most occurring characters

ValueCountFrequency (%)
0 4393
60.2%
. 2431
33.3%
1 469
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4393
90.4%
1 469
 
9.6%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4393
60.2%
. 2431
33.3%
1 469
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4393
60.2%
. 2431
33.3%
1 469
 
6.4%

sponsor_spd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1569 
1.0
862 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1569
64.5%
1.0 862
35.5%

Length

2023-12-03T11:22:43.765663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:43.860790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1569
64.5%
1.0 862
35.5%

Most occurring characters

ValueCountFrequency (%)
0 4000
54.8%
. 2431
33.3%
1 862
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4000
82.3%
1 862
 
17.7%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4000
54.8%
. 2431
33.3%
1 862
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4000
54.8%
. 2431
33.3%
1 862
 
11.8%

sponsor_fdp
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1857 
1.0
574 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1857
76.4%
1.0 574
 
23.6%

Length

2023-12-03T11:22:43.942789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:44.190449image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1857
76.4%
1.0 574
 
23.6%

Most occurring characters

ValueCountFrequency (%)
0 4288
58.8%
. 2431
33.3%
1 574
 
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4288
88.2%
1 574
 
11.8%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4288
58.8%
. 2431
33.3%
1 574
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4288
58.8%
. 2431
33.3%
1 574
 
7.9%

sponsor_cducsu
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1734 
1.0
697 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1734
71.3%
1.0 697
28.7%

Length

2023-12-03T11:22:44.270740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:44.367611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1734
71.3%
1.0 697
28.7%

Most occurring characters

ValueCountFrequency (%)
0 4165
57.1%
. 2431
33.3%
1 697
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4165
85.7%
1 697
 
14.3%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4165
57.1%
. 2431
33.3%
1 697
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4165
57.1%
. 2431
33.3%
1 697
 
9.6%

sponsor_dsu
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2429 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2429
99.9%
1.0 2
 
0.1%

Length

2023-12-03T11:22:44.448051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:44.540048image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2429
99.9%
1.0 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 4860
66.6%
. 2431
33.3%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4860
> 99.9%
1 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4860
66.6%
. 2431
33.3%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4860
66.6%
. 2431
33.3%
1 2
 
< 0.1%

sponsor_gbbhe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2403 
1.0
 
28

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2403
98.8%
1.0 28
 
1.2%

Length

2023-12-03T11:22:44.618868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:44.712500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2403
98.8%
1.0 28
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 4834
66.3%
. 2431
33.3%
1 28
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4834
99.4%
1 28
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4834
66.3%
. 2431
33.3%
1 28
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4834
66.3%
. 2431
33.3%
1 28
 
0.4%

sponsor_dafvp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2427 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2427
99.8%
1.0 4
 
0.2%

Length

2023-12-03T11:22:44.793042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:44.885556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2427
99.8%
1.0 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4858
66.6%
. 2431
33.3%
1 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4858
99.9%
1 4
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4858
66.6%
. 2431
33.3%
1 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4858
66.6%
. 2431
33.3%
1 4
 
0.1%

sponsor_dp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2350 
1.0
 
81

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 2350
96.7%
1.0 81
 
3.3%

Length

2023-12-03T11:22:44.964364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:45.057393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2350
96.7%
1.0 81
 
3.3%

Most occurring characters

ValueCountFrequency (%)
0 4781
65.6%
. 2431
33.3%
1 81
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4781
98.3%
1 81
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4781
65.6%
. 2431
33.3%
1 81
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4781
65.6%
. 2431
33.3%
1 81
 
1.1%

sponsor_fu
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2423 
1.0
 
8

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2423
99.7%
1.0 8
 
0.3%

Length

2023-12-03T11:22:45.137394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:45.231222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2423
99.7%
1.0 8
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4854
66.6%
. 2431
33.3%
1 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4854
99.8%
1 8
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4854
66.6%
. 2431
33.3%
1 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4854
66.6%
. 2431
33.3%
1 8
 
0.1%

sponsor_noparty
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2146 
1.0
285 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2146
88.3%
1.0 285
 
11.7%

Length

2023-12-03T11:22:45.311225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:45.408219image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2146
88.3%
1.0 285
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 4577
62.8%
. 2431
33.3%
1 285
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4577
94.1%
1 285
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4577
62.8%
. 2431
33.3%
1 285
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4577
62.8%
. 2431
33.3%
1 285
 
3.9%

sponsor_govall
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1728 
1.0
703 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1728
71.1%
1.0 703
28.9%

Length

2023-12-03T11:22:45.489496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:45.584296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1728
71.1%
1.0 703
28.9%

Most occurring characters

ValueCountFrequency (%)
0 4159
57.0%
. 2431
33.3%
1 703
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4159
85.5%
1 703
 
14.5%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4159
57.0%
. 2431
33.3%
1 703
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4159
57.0%
. 2431
33.3%
1 703
 
9.6%

sponsor_govone
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1645 
1.0
786 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1645
67.7%
1.0 786
32.3%

Length

2023-12-03T11:22:45.664295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:45.759295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1645
67.7%
1.0 786
32.3%

Most occurring characters

ValueCountFrequency (%)
0 4076
55.9%
. 2431
33.3%
1 786
 
10.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4076
83.8%
1 786
 
16.2%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4076
55.9%
. 2431
33.3%
1 786
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4076
55.9%
. 2431
33.3%
1 786
 
10.8%

sponsor_mps
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2276 
1.0
 
155

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2276
93.6%
1.0 155
 
6.4%

Length

2023-12-03T11:22:45.840122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:45.931915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2276
93.6%
1.0 155
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 4707
64.5%
. 2431
33.3%
1 155
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4707
96.8%
1 155
 
3.2%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4707
64.5%
. 2431
33.3%
1 155
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4707
64.5%
. 2431
33.3%
1 155
 
2.1%

sponsor_afd
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.8%
Missing2187
Missing (%)90.0%
Memory size19.1 KiB
0.0
224 
1.0
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters732
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 224
 
9.2%
1.0 20
 
0.8%
(Missing) 2187
90.0%

Length

2023-12-03T11:22:46.014601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:46.110601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 224
91.8%
1.0 20
 
8.2%

Most occurring characters

ValueCountFrequency (%)
0 468
63.9%
. 244
33.3%
1 20
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 488
66.7%
Other Punctuation 244
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 468
95.9%
1 20
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 468
63.9%
. 244
33.3%
1 20
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 468
63.9%
. 244
33.3%
1 20
 
2.7%

request1
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
unknown
829 
SPD
601 
CDU/CSU
360 
Greens
191 
FDP
135 
Other values (11)
315 

Length

Max length17
Median length12
Mean length5.6149733
Min length2

Characters and Unicode

Total characters13650
Distinct characters32
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowCDU/CSU
2nd rowCDU/CSU
3rd rowFDP
4th rowCDU/CSU
5th rowKPD

Common Values

ValueCountFrequency (%)
unknown 829
34.1%
SPD 601
24.7%
CDU/CSU 360
14.8%
Greens 191
 
7.9%
FDP 135
 
5.6%
Left/PDS 105
 
4.3%
Gr��ne 60
 
2.5%
AfD 60
 
2.5%
Linke 54
 
2.2%
Council of Elders 10
 
0.4%
Other values (6) 26
 
1.1%

Length

2023-12-03T11:22:46.202706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown 829
33.7%
spd 601
24.5%
cdu/csu 360
14.6%
greens 191
 
7.8%
fdp 135
 
5.5%
left/pds 105
 
4.3%
gr��ne 60
 
2.4%
afd 60
 
2.4%
linke 54
 
2.2%
elders 10
 
0.4%
Other values (9) 53
 
2.2%

Most occurring characters

ValueCountFrequency (%)
n 2803
20.5%
D 1269
9.3%
S 1066
 
7.8%
k 883
 
6.5%
P 863
 
6.3%
o 849
 
6.2%
u 840
 
6.2%
w 829
 
6.1%
C 730
 
5.3%
U 725
 
5.3%
Other values (22) 2793
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7727
56.6%
Uppercase Letter 5306
38.9%
Other Punctuation 470
 
3.4%
Other Symbol 120
 
0.9%
Space Separator 27
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 2803
36.3%
k 883
 
11.4%
o 849
 
11.0%
u 840
 
10.9%
w 829
 
10.7%
e 626
 
8.1%
r 268
 
3.5%
s 215
 
2.8%
f 177
 
2.3%
t 106
 
1.4%
Other values (6) 131
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
D 1269
23.9%
S 1066
20.1%
P 863
16.3%
C 730
13.8%
U 725
13.7%
G 263
 
5.0%
L 159
 
3.0%
F 140
 
2.6%
A 60
 
1.1%
E 15
 
0.3%
Other values (3) 16
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 470
100.0%
Other Symbol
ValueCountFrequency (%)
� 120
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13033
95.5%
Common 617
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 2803
21.5%
D 1269
9.7%
S 1066
 
8.2%
k 883
 
6.8%
P 863
 
6.6%
o 849
 
6.5%
u 840
 
6.4%
w 829
 
6.4%
C 730
 
5.6%
U 725
 
5.6%
Other values (19) 2176
16.7%
Common
ValueCountFrequency (%)
/ 470
76.2%
� 120
 
19.4%
27
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13530
99.1%
Specials 120
 
0.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 2803
20.7%
D 1269
9.4%
S 1066
 
7.9%
k 883
 
6.5%
P 863
 
6.4%
o 849
 
6.3%
u 840
 
6.2%
w 829
 
6.1%
C 730
 
5.4%
U 725
 
5.4%
Other values (21) 2673
19.8%
Specials
ValueCountFrequency (%)
� 120
100.0%

request2
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)4.0%
Missing2208
Missing (%)90.8%
Memory size19.1 KiB
FDP
88 
Greens
68 
SPD
48 
CDU/CSU
 
7
DP
 
4
Other values (4)
 
8

Length

Max length8
Median length3
Mean length4.1300448
Min length2

Characters and Unicode

Total characters921
Distinct characters21
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st rowFDP
2nd rowFDP
3rd rowFDP
4th rowFDP
5th rowFDP

Common Values

ValueCountFrequency (%)
FDP 88
 
3.6%
Greens 68
 
2.8%
SPD 48
 
2.0%
CDU/CSU 7
 
0.3%
DP 4
 
0.2%
Gr��ne 3
 
0.1%
GB/BHE 2
 
0.1%
Linke 2
 
0.1%
Left/PDS 1
 
< 0.1%
(Missing) 2208
90.8%

Length

2023-12-03T11:22:46.319744image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:46.463432image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
fdp 88
39.5%
greens 68
30.5%
spd 48
21.5%
cdu/csu 7
 
3.1%
dp 4
 
1.8%
gr��ne 3
 
1.3%
gb/bhe 2
 
0.9%
linke 2
 
0.9%
left/pds 1
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D 148
16.1%
e 142
15.4%
P 141
15.3%
F 88
9.6%
G 73
7.9%
n 73
7.9%
r 71
7.7%
s 68
7.4%
S 56
 
6.1%
U 14
 
1.5%
Other values (11) 47
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 545
59.2%
Lowercase Letter 360
39.1%
Other Punctuation 10
 
1.1%
Other Symbol 6
 
0.7%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 148
27.2%
P 141
25.9%
F 88
16.1%
G 73
13.4%
S 56
 
10.3%
U 14
 
2.6%
C 14
 
2.6%
B 4
 
0.7%
L 3
 
0.6%
H 2
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
e 142
39.4%
n 73
20.3%
r 71
19.7%
s 68
18.9%
i 2
 
0.6%
k 2
 
0.6%
f 1
 
0.3%
t 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
/ 10
100.0%
Other Symbol
ValueCountFrequency (%)
� 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 905
98.3%
Common 16
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 148
16.4%
e 142
15.7%
P 141
15.6%
F 88
9.7%
G 73
8.1%
n 73
8.1%
r 71
7.8%
s 68
7.5%
S 56
 
6.2%
U 14
 
1.5%
Other values (9) 31
 
3.4%
Common
ValueCountFrequency (%)
/ 10
62.5%
� 6
37.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 915
99.3%
Specials 6
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 148
16.2%
e 142
15.5%
P 141
15.4%
F 88
9.6%
G 73
8.0%
n 73
8.0%
r 71
7.8%
s 68
7.4%
S 56
 
6.1%
U 14
 
1.5%
Other values (10) 41
 
4.5%
Specials
ValueCountFrequency (%)
� 6
100.0%

request3
Categorical

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)25.9%
Missing2404
Missing (%)98.9%
Memory size19.1 KiB
SPD
10 
DP
FDP
Greens
FVP
Other values (2)

Length

Max length6
Median length3
Mean length3.3703704
Min length2

Characters and Unicode

Total characters91
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)7.4%

Sample

1st rowDP
2nd rowDP
3rd rowDP
4th rowDP
5th rowDA

Common Values

ValueCountFrequency (%)
SPD 10
 
0.4%
DP 4
 
0.2%
FDP 4
 
0.2%
Greens 4
 
0.2%
FVP 3
 
0.1%
DA 1
 
< 0.1%
Gr��ne 1
 
< 0.1%
(Missing) 2404
98.9%

Length

2023-12-03T11:22:46.594691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:46.724689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
spd 10
37.0%
dp 4
 
14.8%
fdp 4
 
14.8%
greens 4
 
14.8%
fvp 3
 
11.1%
da 1
 
3.7%
gr��ne 1
 
3.7%

Most occurring characters

ValueCountFrequency (%)
P 21
23.1%
D 19
20.9%
S 10
11.0%
e 9
9.9%
F 7
 
7.7%
G 5
 
5.5%
r 5
 
5.5%
n 5
 
5.5%
s 4
 
4.4%
V 3
 
3.3%
Other values (2) 3
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 66
72.5%
Lowercase Letter 23
 
25.3%
Other Symbol 2
 
2.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
P 21
31.8%
D 19
28.8%
S 10
15.2%
F 7
 
10.6%
G 5
 
7.6%
V 3
 
4.5%
A 1
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
e 9
39.1%
r 5
21.7%
n 5
21.7%
s 4
17.4%
Other Symbol
ValueCountFrequency (%)
� 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 89
97.8%
Common 2
 
2.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 21
23.6%
D 19
21.3%
S 10
11.2%
e 9
10.1%
F 7
 
7.9%
G 5
 
5.6%
r 5
 
5.6%
n 5
 
5.6%
s 4
 
4.5%
V 3
 
3.4%
Common
ValueCountFrequency (%)
� 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89
97.8%
Specials 2
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 21
23.6%
D 19
21.3%
S 10
11.2%
e 9
10.1%
F 7
 
7.9%
G 5
 
5.6%
r 5
 
5.6%
n 5
 
5.6%
s 4
 
4.5%
V 3
 
3.4%
Specials
ValueCountFrequency (%)
� 2
100.0%

request4
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)1.4%
Missing2209
Missing (%)90.9%
Memory size19.1 KiB
.
216 
Greens
 
4
Left/PDS
 
2

Length

Max length8
Median length1
Mean length1.1531532
Min length1

Characters and Unicode

Total characters256
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGreens
2nd rowGreens
3rd rowGreens
4th rowGreens
5th rowLeft/PDS

Common Values

ValueCountFrequency (%)
. 216
 
8.9%
Greens 4
 
0.2%
Left/PDS 2
 
0.1%
(Missing) 2209
90.9%

Length

2023-12-03T11:22:46.842690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:46.962690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
216
97.3%
greens 4
 
1.8%
left/pds 2
 
0.9%

Most occurring characters

ValueCountFrequency (%)
. 216
84.4%
e 10
 
3.9%
G 4
 
1.6%
r 4
 
1.6%
n 4
 
1.6%
s 4
 
1.6%
L 2
 
0.8%
f 2
 
0.8%
t 2
 
0.8%
/ 2
 
0.8%
Other values (3) 6
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 218
85.2%
Lowercase Letter 26
 
10.2%
Uppercase Letter 12
 
4.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10
38.5%
r 4
 
15.4%
n 4
 
15.4%
s 4
 
15.4%
f 2
 
7.7%
t 2
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
G 4
33.3%
L 2
16.7%
P 2
16.7%
D 2
16.7%
S 2
16.7%
Other Punctuation
ValueCountFrequency (%)
. 216
99.1%
/ 2
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 218
85.2%
Latin 38
 
14.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10
26.3%
G 4
 
10.5%
r 4
 
10.5%
n 4
 
10.5%
s 4
 
10.5%
L 2
 
5.3%
f 2
 
5.3%
t 2
 
5.3%
P 2
 
5.3%
D 2
 
5.3%
Common
ValueCountFrequency (%)
. 216
99.1%
/ 2
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 216
84.4%
e 10
 
3.9%
G 4
 
1.6%
r 4
 
1.6%
n 4
 
1.6%
s 4
 
1.6%
L 2
 
0.8%
f 2
 
0.8%
t 2
 
0.8%
/ 2
 
0.8%
Other values (3) 6
 
2.3%

request_kpd
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2430 
1.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 2430
> 99.9%
1.0 1
 
< 0.1%

Length

2023-12-03T11:22:47.060689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:47.166690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2430
> 99.9%
1.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 4861
66.7%
. 2431
33.3%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4861
> 99.9%
1 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4861
66.7%
. 2431
33.3%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4861
66.7%
. 2431
33.3%
1 1
 
< 0.1%

request_leftpds
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2305 
1.0
 
126

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2305
94.8%
1.0 126
 
5.2%

Length

2023-12-03T11:22:47.248691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:47.345272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2305
94.8%
1.0 126
 
5.2%

Most occurring characters

ValueCountFrequency (%)
0 4736
64.9%
. 2431
33.3%
1 126
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4736
97.4%
1 126
 
2.6%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4736
64.9%
. 2431
33.3%
1 126
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4736
64.9%
. 2431
33.3%
1 126
 
1.7%

request_greens
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2143 
1.0
288 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2143
88.2%
1.0 288
 
11.8%

Length

2023-12-03T11:22:47.429271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:47.529240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2143
88.2%
1.0 288
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 4574
62.7%
. 2431
33.3%
1 288
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4574
94.1%
1 288
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4574
62.7%
. 2431
33.3%
1 288
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4574
62.7%
. 2431
33.3%
1 288
 
3.9%

request_spd
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1727 
1.0
704 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1727
71.0%
1.0 704
29.0%

Length

2023-12-03T11:22:47.614240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:47.713272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1727
71.0%
1.0 704
29.0%

Most occurring characters

ValueCountFrequency (%)
0 4158
57.0%
. 2431
33.3%
1 704
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4158
85.5%
1 704
 
14.5%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4158
57.0%
. 2431
33.3%
1 704
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4158
57.0%
. 2431
33.3%
1 704
 
9.7%

request_fdp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2249 
1.0
 
182

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2249
92.5%
1.0 182
 
7.5%

Length

2023-12-03T11:22:47.795357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:47.891361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2249
92.5%
1.0 182
 
7.5%

Most occurring characters

ValueCountFrequency (%)
0 4680
64.2%
. 2431
33.3%
1 182
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4680
96.3%
1 182
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4680
64.2%
. 2431
33.3%
1 182
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4680
64.2%
. 2431
33.3%
1 182
 
2.5%

request_cducsu
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2064 
1.0
367 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2064
84.9%
1.0 367
 
15.1%

Length

2023-12-03T11:22:47.971956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:48.070401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2064
84.9%
1.0 367
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 4495
61.6%
. 2431
33.3%
1 367
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4495
92.5%
1 367
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4495
61.6%
. 2431
33.3%
1 367
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4495
61.6%
. 2431
33.3%
1 367
 
5.0%

request_gbbhe
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2424 
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2424
99.7%
1.0 7
 
0.3%

Length

2023-12-03T11:22:48.152958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:48.249314image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2424
99.7%
1.0 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4855
66.6%
. 2431
33.3%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4855
99.9%
1 7
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4855
66.6%
. 2431
33.3%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4855
66.6%
. 2431
33.3%
1 7
 
0.1%

request_dafvp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2427 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2427
99.8%
1.0 4
 
0.2%

Length

2023-12-03T11:22:48.333955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:48.430222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2427
99.8%
1.0 4
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4858
66.6%
. 2431
33.3%
1 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4858
99.9%
1 4
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4858
66.6%
. 2431
33.3%
1 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4858
66.6%
. 2431
33.3%
1 4
 
0.1%

request_dp
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2416 
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2416
99.4%
1.0 15
 
0.6%

Length

2023-12-03T11:22:48.511160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:48.607162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2416
99.4%
1.0 15
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 4847
66.5%
. 2431
33.3%
1 15
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4847
99.7%
1 15
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4847
66.5%
. 2431
33.3%
1 15
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4847
66.5%
. 2431
33.3%
1 15
 
0.2%

request_fu
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2426 
1.0
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2426
99.8%
1.0 5
 
0.2%

Length

2023-12-03T11:22:48.686945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:48.785949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2426
99.8%
1.0 5
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 4857
66.6%
. 2431
33.3%
1 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4857
99.9%
1 5
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4857
66.6%
. 2431
33.3%
1 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4857
66.6%
. 2431
33.3%
1 5
 
0.1%

request_afd
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.8%
Missing2187
Missing (%)90.0%
Memory size19.1 KiB
0.0
184 
1.0
60 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters732
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 184
 
7.6%
1.0 60
 
2.5%
(Missing) 2187
90.0%

Length

2023-12-03T11:22:48.867951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:48.962207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 184
75.4%
1.0 60
 
24.6%

Most occurring characters

ValueCountFrequency (%)
0 428
58.5%
. 244
33.3%
1 60
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 488
66.7%
Other Punctuation 244
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 428
87.7%
1 60
 
12.3%
Other Punctuation
ValueCountFrequency (%)
. 244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 732
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 428
58.5%
. 244
33.3%
1 60
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 732
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 428
58.5%
. 244
33.3%
1 60
 
8.2%

request_noparty
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
2413 
1.0
 
18

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2413
99.3%
1.0 18
 
0.7%

Length

2023-12-03T11:22:49.046178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:49.139855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2413
99.3%
1.0 18
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 4844
66.4%
. 2431
33.3%
1 18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4844
99.6%
1 18
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4844
66.4%
. 2431
33.3%
1 18
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4844
66.4%
. 2431
33.3%
1 18
 
0.2%

request_unknown
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1602 
1.0
829 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters7293
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1602
65.9%
1.0 829
34.1%

Length

2023-12-03T11:22:49.219880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:49.316878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1602
65.9%
1.0 829
34.1%

Most occurring characters

ValueCountFrequency (%)
0 4033
55.3%
. 2431
33.3%
1 829
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4862
66.7%
Other Punctuation 2431
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4033
82.9%
1 829
 
17.1%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7293
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4033
55.3%
. 2431
33.3%
1 829
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7293
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4033
55.3%
. 2431
33.3%
1 829
 
11.4%

request_gov
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1450 
99.0
829 
1.0
152 

Length

Max length4
Median length3
Mean length3.3410119
Min length3

Characters and Unicode

Total characters8122
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1450
59.6%
99.0 829
34.1%
1.0 152
 
6.3%

Length

2023-12-03T11:22:49.402880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:49.503880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1450
59.6%
99.0 829
34.1%
1.0 152
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 3881
47.8%
. 2431
29.9%
9 1658
20.4%
1 152
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5691
70.1%
Other Punctuation 2431
29.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3881
68.2%
9 1658
29.1%
1 152
 
2.7%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8122
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3881
47.8%
. 2431
29.9%
9 1658
20.4%
1 152
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8122
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3881
47.8%
. 2431
29.9%
9 1658
20.4%
1 152
 
1.9%

request_govpart
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0.0
1203 
99.0
839 
1.0
389 

Length

Max length4
Median length3
Mean length3.3451255
Min length3

Characters and Unicode

Total characters8132
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1203
49.5%
99.0 839
34.5%
1.0 389
 
16.0%

Length

2023-12-03T11:22:49.596196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:49.715209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1203
49.5%
99.0 839
34.5%
1.0 389
 
16.0%

Most occurring characters

ValueCountFrequency (%)
0 3634
44.7%
. 2431
29.9%
9 1678
20.6%
1 389
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5701
70.1%
Other Punctuation 2431
29.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3634
63.7%
9 1678
29.4%
1 389
 
6.8%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3634
44.7%
. 2431
29.9%
9 1678
20.6%
1 389
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3634
44.7%
. 2431
29.9%
9 1678
20.6%
1 389
 
4.8%

request_oppo
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
1.0
1066 
99.0
839 
0.0
526 

Length

Max length4
Median length3
Mean length3.3451255
Min length3

Characters and Unicode

Total characters8132
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1066
43.9%
99.0 839
34.5%
0.0 526
21.6%

Length

2023-12-03T11:22:49.810202image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:49.918384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1066
43.9%
99.0 839
34.5%
0.0 526
21.6%

Most occurring characters

ValueCountFrequency (%)
0 2957
36.4%
. 2431
29.9%
9 1678
20.6%
1 1066
 
13.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5701
70.1%
Other Punctuation 2431
29.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2957
51.9%
9 1678
29.4%
1 1066
 
18.7%
Other Punctuation
ValueCountFrequency (%)
. 2431
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2957
36.4%
. 2431
29.9%
9 1678
20.6%
1 1066
 
13.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2957
36.4%
. 2431
29.9%
9 1678
20.6%
1 1066
 
13.1%

request_govoppo
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.1%
Missing86
Missing (%)3.5%
Memory size19.1 KiB
0.0
1464 
99.0
839 
1.0
 
42

Length

Max length4
Median length3
Mean length3.3577825
Min length3

Characters and Unicode

Total characters7874
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1464
60.2%
99.0 839
34.5%
1.0 42
 
1.7%
(Missing) 86
 
3.5%

Length

2023-12-03T11:22:50.013932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:50.122813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1464
62.4%
99.0 839
35.8%
1.0 42
 
1.8%

Most occurring characters

ValueCountFrequency (%)
0 3809
48.4%
. 2345
29.8%
9 1678
21.3%
1 42
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5529
70.2%
Other Punctuation 2345
29.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3809
68.9%
9 1678
30.3%
1 42
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 2345
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7874
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3809
48.4%
. 2345
29.8%
9 1678
21.3%
1 42
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7874
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3809
48.4%
. 2345
29.8%
9 1678
21.3%
1 42
 
0.5%

free_vote
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0
2296 
1
 
135

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2431
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2296
94.4%
1 135
 
5.6%

Length

2023-12-03T11:22:50.208859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:50.305195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 2296
94.4%
1 135
 
5.6%

Most occurring characters

ValueCountFrequency (%)
0 2296
94.4%
1 135
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2431
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2296
94.4%
1 135
 
5.6%

Most occurring scripts

ValueCountFrequency (%)
Common 2431
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2296
94.4%
1 135
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2431
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2296
94.4%
1 135
 
5.6%

bundesrat
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
0
909 
1
579 
2
496 
99
447 

Length

Max length2
Median length1
Mean length1.1838749
Min length1

Characters and Unicode

Total characters2878
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row99
2nd row99
3rd row99
4th row99
5th row99

Common Values

ValueCountFrequency (%)
0 909
37.4%
1 579
23.8%
2 496
20.4%
99 447
18.4%

Length

2023-12-03T11:22:50.385195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:50.489207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0 909
37.4%
1 579
23.8%
2 496
20.4%
99 447
18.4%

Most occurring characters

ValueCountFrequency (%)
0 909
31.6%
9 894
31.1%
1 579
20.1%
2 496
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2878
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 909
31.6%
9 894
31.1%
1 579
20.1%
2 496
17.2%

Most occurring scripts

ValueCountFrequency (%)
Common 2878
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 909
31.6%
9 894
31.1%
1 579
20.1%
2 496
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 909
31.6%
9 894
31.1%
1 579
20.1%
2 496
17.2%

gesta
Text

MISSING 

Distinct470
Distinct (%)43.7%
Missing1356
Missing (%)55.8%
Memory size19.1 KiB
2023-12-03T11:22:50.832086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0930233
Min length2

Characters and Unicode

Total characters3325
Distinct characters34
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique290 ?
Unique (%)27.0%

Sample

1st rowL1
2nd rowA1
3rd rowC13
4th rowD19
5th rowD19
ValueCountFrequency (%)
g36 45
 
4.2%
d3 23
 
2.1%
d25 20
 
1.9%
b69 19
 
1.8%
d13 18
 
1.7%
c169 16
 
1.5%
n7 14
 
1.3%
d1 12
 
1.1%
d2 12
 
1.1%
d6 12
 
1.1%
Other values (457) 886
82.3%
2023-12-03T11:22:51.160054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
D 387
11.6%
1 357
10.7%
3 282
 
8.5%
2 281
 
8.5%
0 274
 
8.2%
6 235
 
7.1%
5 216
 
6.5%
4 174
 
5.2%
G 170
 
5.1%
C 157
 
4.7%
Other values (24) 792
23.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2203
66.3%
Uppercase Letter 1098
33.0%
Space Separator 14
 
0.4%
Other Symbol 8
 
0.2%
Lowercase Letter 2
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 387
35.2%
G 170
15.5%
C 157
14.3%
B 112
 
10.2%
M 53
 
4.8%
N 34
 
3.1%
E 29
 
2.6%
X 25
 
2.3%
K 24
 
2.2%
I 23
 
2.1%
Other values (10) 84
 
7.7%
Decimal Number
ValueCountFrequency (%)
1 357
16.2%
3 282
12.8%
2 281
12.8%
0 274
12.4%
6 235
10.7%
5 216
9.8%
4 174
7.9%
7 139
 
6.3%
9 136
 
6.2%
8 109
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
g 1
50.0%
j 1
50.0%
Space Separator
ValueCountFrequency (%)
14
100.0%
Other Symbol
ValueCountFrequency (%)
� 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2225
66.9%
Latin 1100
33.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 387
35.2%
G 170
15.5%
C 157
14.3%
B 112
 
10.2%
M 53
 
4.8%
N 34
 
3.1%
E 29
 
2.6%
X 25
 
2.3%
K 24
 
2.2%
I 23
 
2.1%
Other values (12) 86
 
7.8%
Common
ValueCountFrequency (%)
1 357
16.0%
3 282
12.7%
2 281
12.6%
0 274
12.3%
6 235
10.6%
5 216
9.7%
4 174
7.8%
7 139
 
6.2%
9 136
 
6.1%
8 109
 
4.9%
Other values (2) 22
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3317
99.8%
Specials 8
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 387
11.7%
1 357
10.8%
3 282
 
8.5%
2 281
 
8.5%
0 274
 
8.3%
6 235
 
7.1%
5 216
 
6.5%
4 174
 
5.2%
G 170
 
5.1%
C 157
 
4.7%
Other values (23) 784
23.6%
Specials
ValueCountFrequency (%)
� 8
100.0%

cabid_parlgov
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean573.36569
Minimum31
Maximum1528
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:51.283844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile85
Q1252
median472
Q3862
95-th percentile1528
Maximum1528
Range1497
Interquartile range (IQR)610

Descriptive statistics

Standard deviation435.69096
Coefficient of variation (CV)0.7598832
Kurtosis-0.17872446
Mean573.36569
Median Absolute Deviation (MAD)306
Skewness0.87826611
Sum1393852
Variance189826.61
MonotonicityNot monotonic
2023-12-03T11:22:51.390658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
472 275
11.3%
1528 232
9.5%
286 218
 
9.0%
1071 216
 
8.9%
778 180
 
7.4%
85 177
 
7.3%
107 168
 
6.9%
346 135
 
5.6%
147 133
 
5.5%
862 129
 
5.3%
Other values (17) 568
23.4%
ValueCountFrequency (%)
31 27
 
1.1%
62 10
 
0.4%
85 177
7.3%
107 168
6.9%
147 133
5.5%
170 23
 
0.9%
192 66
 
2.7%
252 36
 
1.5%
286 218
9.0%
326 1
 
< 0.1%
ValueCountFrequency (%)
1528 232
9.5%
1515 12
 
0.5%
1071 216
8.9%
906 5
 
0.2%
871 19
 
0.8%
862 129
5.3%
793 41
 
1.7%
778 180
7.4%
642 5
 
0.2%
633 87
 
3.6%

cabid_erdda
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)1.4%
Missing460
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean620.15018
Minimum601
Maximum629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.1 KiB
2023-12-03T11:22:51.502028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum601
5-th percentile601
Q1616
median624
Q3627
95-th percentile629
Maximum629
Range28
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.2121512
Coefficient of variation (CV)0.01485471
Kurtosis-0.29607823
Mean620.15018
Median Absolute Deviation (MAD)4
Skewness-1.061742
Sum1222316
Variance84.86373
MonotonicityNot monotonic
2023-12-03T11:22:51.601621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
629 275
11.3%
622 209
8.6%
625 180
 
7.4%
628 177
 
7.3%
626 168
 
6.9%
621 135
 
5.6%
601 133
 
5.5%
624 129
 
5.3%
627 102
 
4.2%
604 87
 
3.6%
Other values (17) 376
15.5%
(Missing) 460
18.9%
ValueCountFrequency (%)
601 133
5.5%
602 66
2.7%
603 16
 
0.7%
604 87
3.6%
605 41
 
1.7%
606 5
 
0.2%
607 6
 
0.2%
608 1
 
< 0.1%
609 3
 
0.1%
610 27
 
1.1%
ValueCountFrequency (%)
629 275
11.3%
628 177
7.3%
627 102
 
4.2%
626 168
6.9%
625 180
7.4%
624 129
5.3%
623 9
 
0.4%
622 209
8.6%
621 135
5.6%
620 5
 
0.2%

cabinet
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
Merkel II
275 
Merkel IV
244 
Merkel III
216 
Kohl III
209 
Kohl VI
180 
Other values (24)
1307 

Length

Max length13
Median length11
Mean length9.0098725
Min length6

Characters and Unicode

Total characters21903
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowAdenauer I
2nd rowAdenauer I
3rd rowAdenauer I
4th rowAdenauer I
5th rowAdenauer I

Common Values

ValueCountFrequency (%)
Merkel II 275
11.3%
Merkel IV 244
10.0%
Merkel III 216
 
8.9%
Kohl III 209
 
8.6%
Kohl VI 180
 
7.4%
Merkel I 177
 
7.3%
Schroeder I 168
 
6.9%
Kohl II 135
 
5.6%
Adenauer I 133
 
5.5%
Kohl V 129
 
5.3%
Other values (19) 565
23.2%

Length

2023-12-03T11:22:51.717606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
merkel 912
18.8%
kohl 667
13.8%
ii 657
13.6%
i 584
12.1%
iii 462
9.5%
adenauer 358
 
7.4%
iv 340
 
7.0%
schroeder 270
 
5.6%
vi 185
 
3.8%
v 170
 
3.5%
Other values (7) 234
 
4.8%

Most occurring characters

ValueCountFrequency (%)
I 3827
17.5%
e 3126
14.3%
2408
11.0%
r 1946
8.9%
l 1579
 
7.2%
h 1081
 
4.9%
o 937
 
4.3%
M 912
 
4.2%
k 912
 
4.2%
d 829
 
3.8%
Other values (16) 4346
19.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12532
57.2%
Uppercase Letter 6963
31.8%
Space Separator 2408
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 3126
24.9%
r 1946
15.5%
l 1579
12.6%
h 1081
 
8.6%
o 937
 
7.5%
k 912
 
7.3%
d 829
 
6.6%
a 443
 
3.5%
n 438
 
3.5%
c 386
 
3.1%
Other values (6) 855
 
6.8%
Uppercase Letter
ValueCountFrequency (%)
I 3827
55.0%
M 912
 
13.1%
V 702
 
10.1%
K 690
 
9.9%
S 386
 
5.5%
A 358
 
5.1%
B 57
 
0.8%
E 28
 
0.4%
X 3
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 19495
89.0%
Common 2408
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 3827
19.6%
e 3126
16.0%
r 1946
10.0%
l 1579
8.1%
h 1081
 
5.5%
o 937
 
4.8%
M 912
 
4.7%
k 912
 
4.7%
d 829
 
4.3%
V 702
 
3.6%
Other values (15) 3644
18.7%
Common
ValueCountFrequency (%)
2408
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 3827
17.5%
e 3126
14.3%
2408
11.0%
r 1946
8.9%
l 1579
 
7.2%
h 1081
 
4.9%
o 937
 
4.3%
M 912
 
4.2%
k 912
 
4.2%
d 829
 
3.8%
Other values (16) 4346
19.8%

cab_start
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2009-10-28
275 
2018-03-14
244 
2013-12-17
216 
1987-03-11
210 
1994-11-15
180 
Other values (24)
1306 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters24310
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row1949-09-15
2nd row1949-09-15
3rd row1949-09-15
4th row1949-09-15
5th row1949-09-15

Common Values

ValueCountFrequency (%)
2009-10-28 275
11.3%
2018-03-14 244
10.0%
2013-12-17 216
 
8.9%
1987-03-11 210
 
8.6%
1994-11-15 180
 
7.4%
2005-11-22 177
 
7.3%
1998-10-27 168
 
6.9%
1983-03-29 134
 
5.5%
1949-09-15 133
 
5.5%
1991-01-17 130
 
5.3%
Other values (19) 564
23.2%

Length

2023-12-03T11:22:51.825453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2009-10-28 275
11.3%
2018-03-14 244
10.0%
2013-12-17 216
 
8.9%
1987-03-11 210
 
8.6%
1994-11-15 180
 
7.4%
2005-11-22 177
 
7.3%
1998-10-27 168
 
6.9%
1983-03-29 134
 
5.5%
1949-09-15 133
 
5.5%
1991-01-17 130
 
5.3%
Other values (19) 564
23.2%

Most occurring characters

ValueCountFrequency (%)
1 5498
22.6%
- 4862
20.0%
0 3463
14.2%
2 2914
12.0%
9 2683
11.0%
3 1059
 
4.4%
8 1057
 
4.3%
5 920
 
3.8%
7 907
 
3.7%
4 612
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19448
80.0%
Dash Punctuation 4862
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5498
28.3%
0 3463
17.8%
2 2914
15.0%
9 2683
13.8%
3 1059
 
5.4%
8 1057
 
5.4%
5 920
 
4.7%
7 907
 
4.7%
4 612
 
3.1%
6 335
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 4862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5498
22.6%
- 4862
20.0%
0 3463
14.2%
2 2914
12.0%
9 2683
11.0%
3 1059
 
4.4%
8 1057
 
4.3%
5 920
 
3.8%
7 907
 
3.7%
4 612
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5498
22.6%
- 4862
20.0%
0 3463
14.2%
2 2914
12.0%
9 2683
11.0%
3 1059
 
4.4%
8 1057
 
4.3%
5 920
 
3.8%
7 907
 
3.7%
4 612
 
2.5%

cab_end
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
2013-09-22
275 
2021-10-26
244 
2017-10-23
216 
1990-10-30
210 
1998-09-27
180 
Other values (24)
1306 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters24310
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row1953-09-06
2nd row1953-09-06
3rd row1953-09-06
4th row1953-09-06
5th row1953-09-06

Common Values

ValueCountFrequency (%)
2013-09-22 275
11.3%
2021-10-26 244
10.0%
2017-10-23 216
 
8.9%
1990-10-30 210
 
8.6%
1998-09-27 180
 
7.4%
2009-09-27 177
 
7.3%
2002-09-22 168
 
6.9%
1987-01-25 134
 
5.5%
1953-09-06 133
 
5.5%
1994-10-16 130
 
5.3%
Other values (19) 564
23.2%

Length

2023-12-03T11:22:51.919459image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-09-22 275
11.3%
2021-10-26 244
10.0%
2017-10-23 216
 
8.9%
1990-10-30 210
 
8.6%
1998-09-27 180
 
7.4%
2009-09-27 177
 
7.3%
2002-09-22 168
 
6.9%
1987-01-25 134
 
5.5%
1953-09-06 133
 
5.5%
1994-10-16 130
 
5.3%
Other values (19) 564
23.2%

Most occurring characters

ValueCountFrequency (%)
- 4862
20.0%
0 4817
19.8%
2 3679
15.1%
1 3557
14.6%
9 3245
13.3%
7 1019
 
4.2%
3 950
 
3.9%
5 812
 
3.3%
6 695
 
2.9%
8 525
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19448
80.0%
Dash Punctuation 4862
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4817
24.8%
2 3679
18.9%
1 3557
18.3%
9 3245
16.7%
7 1019
 
5.2%
3 950
 
4.9%
5 812
 
4.2%
6 695
 
3.6%
8 525
 
2.7%
4 149
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 4862
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24310
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4862
20.0%
0 4817
19.8%
2 3679
15.1%
1 3557
14.6%
9 3245
13.3%
7 1019
 
4.2%
3 950
 
3.9%
5 812
 
3.3%
6 695
 
2.9%
8 525
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24310
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4862
20.0%
0 4817
19.8%
2 3679
15.1%
1 3557
14.6%
9 3245
13.3%
7 1019
 
4.2%
3 950
 
3.9%
5 812
 
3.3%
6 695
 
2.9%
8 525
 
2.2%

elecper_start
Categorical

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)0.9%
Missing211
Missing (%)8.7%
Memory size19.1 KiB
2009-09-27
275 
2017-10-27
244 
2013-10-27
216 
1987-01-25
210 
1994-10-16
180 
Other values (14)
1095 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters22200
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1949-08-14
2nd row1949-08-14
3rd row1949-08-14
4th row1949-08-14
5th row1949-08-14

Common Values

ValueCountFrequency (%)
2009-09-27 275
11.3%
2017-10-27 244
10.0%
2013-10-27 216
8.9%
1987-01-25 210
8.6%
1994-10-16 180
7.4%
2005-09-18 177
7.3%
1998-09-27 168
6.9%
1983-03-06 134
 
5.5%
1949-08-14 133
 
5.5%
1990-12-02 130
 
5.3%
Other values (9) 353
14.5%
(Missing) 211
8.7%

Length

2023-12-03T11:22:52.011665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2009-09-27 275
12.4%
2017-10-27 244
11.0%
2013-10-27 216
9.7%
1987-01-25 210
9.5%
1994-10-16 180
8.1%
2005-09-18 177
8.0%
1998-09-27 168
7.6%
1983-03-06 134
 
6.0%
1949-08-14 133
 
6.0%
1990-12-02 130
 
5.9%
Other values (9) 353
15.9%

Most occurring characters

ValueCountFrequency (%)
- 4440
20.0%
0 4200
18.9%
1 3327
15.0%
9 3024
13.6%
2 2750
12.4%
7 1482
 
6.7%
8 881
 
4.0%
3 609
 
2.7%
5 557
 
2.5%
6 484
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17760
80.0%
Dash Punctuation 4440
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4200
23.6%
1 3327
18.7%
9 3024
17.0%
2 2750
15.5%
7 1482
 
8.3%
8 881
 
5.0%
3 609
 
3.4%
5 557
 
3.1%
6 484
 
2.7%
4 446
 
2.5%
Dash Punctuation
ValueCountFrequency (%)
- 4440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 22200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4440
20.0%
0 4200
18.9%
1 3327
15.0%
9 3024
13.6%
2 2750
12.4%
7 1482
 
6.7%
8 881
 
4.0%
3 609
 
2.7%
5 557
 
2.5%
6 484
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4440
20.0%
0 4200
18.9%
1 3327
15.0%
9 3024
13.6%
2 2750
12.4%
7 1482
 
6.7%
8 881
 
4.0%
3 609
 
2.7%
5 557
 
2.5%
6 484
 
2.2%

elecper_end
Categorical

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)0.9%
Missing385
Missing (%)15.8%
Memory size19.1 KiB
2013-09-22
275 
2021-10-26
244 
2017-10-23
216 
1998-09-27
180 
2009-09-27
177 
Other values (14)
954 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters20460
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1953-09-06
2nd row1953-09-06
3rd row1953-09-06
4th row1953-09-06
5th row1953-09-06

Common Values

ValueCountFrequency (%)
2013-09-22 275
11.3%
2021-10-26 244
10.0%
2017-10-23 216
8.9%
1998-09-27 180
7.4%
2009-09-27 177
7.3%
2002-09-22 168
6.9%
1987-01-25 134
 
5.5%
1953-09-06 133
 
5.5%
1994-10-16 130
 
5.3%
2005-09-18 102
 
4.2%
Other values (9) 287
11.8%
(Missing) 385
15.8%

Length

2023-12-03T11:22:52.105205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2013-09-22 275
13.4%
2021-10-26 244
11.9%
2017-10-23 216
10.6%
1998-09-27 180
8.8%
2009-09-27 177
8.7%
2002-09-22 168
8.2%
1987-01-25 134
6.5%
1953-09-06 133
6.5%
1994-10-16 130
6.4%
2005-09-18 102
 
5.0%
Other values (9) 287
14.0%

Most occurring characters

ValueCountFrequency (%)
- 4092
20.0%
0 3937
19.2%
2 3508
17.1%
1 2895
14.1%
9 2623
12.8%
7 872
 
4.3%
3 670
 
3.3%
5 627
 
3.1%
6 603
 
2.9%
8 503
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16368
80.0%
Dash Punctuation 4092
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3937
24.1%
2 3508
21.4%
1 2895
17.7%
9 2623
16.0%
7 872
 
5.3%
3 670
 
4.1%
5 627
 
3.8%
6 603
 
3.7%
8 503
 
3.1%
4 130
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 4092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 20460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 4092
20.0%
0 3937
19.2%
2 3508
17.1%
1 2895
14.1%
9 2623
12.8%
7 872
 
4.3%
3 670
 
3.3%
5 627
 
3.1%
6 603
 
2.9%
8 503
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 4092
20.0%
0 3937
19.2%
2 3508
17.1%
1 2895
14.1%
9 2623
12.8%
7 872
 
4.3%
3 670
 
3.3%
5 627
 
3.1%
6 603
 
2.9%
8 503
 
2.5%

cab_parties
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
CDU/CSU, FDP
971 
CDU/CSU, SPD
660 
SPD, GR
270 
SPD, FDP
173 
CDU/CSU, FDP, DP
150 
Other values (5)
207 

Length

Max length24
Median length12
Mean length11.967503
Min length7

Characters and Unicode

Total characters29093
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCDU/CSU, FDP, DP
2nd rowCDU/CSU, FDP, DP
3rd rowCDU/CSU, FDP, DP
4th rowCDU/CSU, FDP, DP
5th rowCDU/CSU, FDP, DP

Common Values

ValueCountFrequency (%)
CDU/CSU, FDP 971
39.9%
CDU/CSU, SPD 660
27.1%
SPD, GR 270
 
11.1%
SPD, FDP 173
 
7.1%
CDU/CSU, FDP, DP 150
 
6.2%
CDU/CSU, DP, DA/FVP 86
 
3.5%
CDU/CSU, FDP, DP, GB/BHE 66
 
2.7%
CDU/CSU, DP 41
 
1.7%
CDU/CSU, FDP, DSU 8
 
0.3%
CDU/CSU 6
 
0.2%

Length

2023-12-03T11:22:52.206135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-03T11:22:52.336480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cdu/csu 1988
38.0%
fdp 1368
26.1%
spd 1103
21.1%
dp 343
 
6.6%
gr 270
 
5.2%
da/fvp 86
 
1.6%
gb/bhe 66
 
1.3%
dsu 8
 
0.2%

Most occurring characters

ValueCountFrequency (%)
D 4896
16.8%
U 3984
13.7%
C 3976
13.7%
S 3099
10.7%
P 2900
10.0%
, 2801
9.6%
2801
9.6%
/ 2140
7.4%
F 1454
 
5.0%
G 336
 
1.2%
Other values (6) 706
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 21351
73.4%
Other Punctuation 4941
 
17.0%
Space Separator 2801
 
9.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D 4896
22.9%
U 3984
18.7%
C 3976
18.6%
S 3099
14.5%
P 2900
13.6%
F 1454
 
6.8%
G 336
 
1.6%
R 270
 
1.3%
B 132
 
0.6%
A 86
 
0.4%
Other values (3) 218
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 2801
56.7%
/ 2140
43.3%
Space Separator
ValueCountFrequency (%)
2801
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 21351
73.4%
Common 7742
 
26.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
D 4896
22.9%
U 3984
18.7%
C 3976
18.6%
S 3099
14.5%
P 2900
13.6%
F 1454
 
6.8%
G 336
 
1.6%
R 270
 
1.3%
B 132
 
0.6%
A 86
 
0.4%
Other values (3) 218
 
1.0%
Common
ValueCountFrequency (%)
, 2801
36.2%
2801
36.2%
/ 2140
27.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29093
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D 4896
16.8%
U 3984
13.7%
C 3976
13.7%
S 3099
10.7%
P 2900
10.0%
, 2801
9.6%
2801
9.6%
/ 2140
7.4%
F 1454
 
5.0%
G 336
 
1.2%
Other values (6) 706
 
2.4%
Distinct1000
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Memory size19.1 KiB
Minimum1950-06-15 00:00:00
Maximum2021-09-07 00:00:00
2023-12-03T11:22:52.474899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:52.598114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-03T11:22:36.288185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:28.868568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.799021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.714442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.643787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.532843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.443702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.490378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.341767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.396104image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:28.976216image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.903726image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.821451image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.746723image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.637850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.550579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.590381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.461773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.499089image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.080281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.004032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.924443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.845157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.736844image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.649070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.691347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.574768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.605741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.185474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.111039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.026461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.943861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.846327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.760100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.789348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.681770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.698754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.283464image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.211855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.124460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.032843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.942328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.853087image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.875349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.781753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.797785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.385393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.310874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.228470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.127619image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.042329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.950350image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.965347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.881819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.900783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.484777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.409370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.334644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.226250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.136359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.045378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.053378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.983827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.992772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.581785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.501341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.429187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.315212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.225330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.273348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.138796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.077799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:37.091754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:29.691760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:30.613175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:31.537783image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:32.429806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:33.335330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:34.381349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:35.241766image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-12-03T11:22:36.189799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-12-03T11:22:52.771525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
vote_idvote_id2elecpervote_typepolicy1policy2policy3cabid_parlgovcabid_erddavote_finalpassagevote_numproposalssponsor1sponsor2sponsor3sponsor4sponsor_kpdsponsor_leftpdssponsor_greenssponsor_spdsponsor_fdpsponsor_cducsusponsor_dsusponsor_gbbhesponsor_dafvpsponsor_dpsponsor_fusponsor_nopartysponsor_govallsponsor_govonesponsor_mpssponsor_afdrequest1request2request3request4request_kpdrequest_leftpdsrequest_greensrequest_spdrequest_fdprequest_cducsurequest_gbbherequest_dafvprequest_dprequest_furequest_afdrequest_nopartyrequest_unknownrequest_govrequest_govpartrequest_opporequest_govoppofree_votebundesratcabinetcab_startcab_endelecper_startelecper_endcab_parties
vote_id1.0000.9780.9750.0980.0330.024-0.2740.4940.9750.0500.9990.4560.3480.2910.3230.0000.0000.0880.0810.0740.0480.0000.0000.0000.0450.0000.2590.0540.0630.2850.0000.4410.5581.0000.0000.0000.0000.0160.0530.0860.0660.0000.0000.0000.0000.0000.6050.1160.0780.1120.1030.1010.4130.1170.5000.5000.5000.5030.4880.250
vote_id20.9781.0000.9960.0830.0250.026-0.2640.4960.9960.0290.1860.3410.6220.6830.9480.0000.2360.0840.1840.2220.0720.0150.1190.0360.2080.0580.1660.0000.0620.1131.0000.4480.5450.3520.9980.0000.2050.0000.3050.0700.1590.0540.0360.0840.0431.0000.2470.3140.2470.2200.2170.2400.0880.3970.7020.7020.7020.7040.7030.526
elecper0.9750.9961.0000.0800.0200.022-0.2830.4941.0000.1410.1110.2660.4750.5480.7910.0000.2980.4070.2960.3340.1560.0680.2800.0890.4480.1400.3420.2150.1930.1811.0000.3320.4310.5020.9980.0000.3000.3420.3820.1640.2890.1290.0890.1710.1041.0000.1680.4040.3200.3180.3140.3070.1690.6020.9950.9960.9960.9980.9980.671
vote_type0.0980.0830.0801.000-0.1030.072-0.0580.0410.0630.7270.7050.4510.2030.1630.1950.0000.0900.0840.1990.3870.4570.0000.0000.0000.0980.0000.2090.6240.6030.1800.0420.3220.1950.3720.1650.0110.0630.0710.0750.0940.1350.0220.0000.0000.0000.0000.3140.1050.1460.1770.1930.1180.3030.3990.2130.2120.2120.2070.1340.103
policy10.0330.0250.020-0.1031.000-0.0270.0350.0300.0140.1090.0080.2030.0720.0340.2610.0000.1970.1370.0240.0700.0900.0000.0220.0000.0700.0000.1390.1300.1030.0850.2600.1850.0990.3420.0680.0000.2480.0950.0000.0400.0000.0000.0280.0580.0000.2020.0420.0610.0600.0520.0510.0610.1350.2430.2140.2190.2190.2100.2040.112
policy20.0240.0260.0220.072-0.0271.000-0.1380.075-0.0680.1450.1680.2240.1110.2730.3091.0000.2580.2040.2250.1590.1871.0000.1090.0000.1450.0000.2640.1430.1760.1860.1280.2030.1790.3030.0000.0000.3050.1880.2420.1100.1200.0000.0450.1130.0000.2760.1930.3710.2640.2670.2670.2560.2170.3140.2480.2480.2480.2350.2480.159
policy3-0.274-0.264-0.283-0.0580.035-0.1381.0000.122-0.4110.1880.3050.2630.0520.5250.4541.0000.4240.1480.2730.2140.1561.0000.1831.0000.3950.0000.2960.2740.2880.4271.0000.1710.0000.7071.0000.0000.3670.2700.3060.0000.1641.0001.0000.0000.0001.0000.0000.3280.2390.2540.2400.2430.4440.4620.4000.4000.4000.4000.3700.355
cabid_parlgov0.4940.4960.4940.0410.0300.0750.1221.0000.0550.0600.0920.2480.4090.3960.6370.0000.1940.2810.1710.2590.0970.0500.1830.1010.1790.0930.2390.1140.1130.3151.0000.3470.3970.6440.9980.0000.2260.2170.2450.1640.1330.0870.1010.0700.0661.0000.2170.2800.2280.2100.2510.2760.1370.2460.9960.9940.9940.9960.9950.564
cabid_erdda0.9750.9961.0000.0630.014-0.068-0.4110.0551.0000.1380.1460.2420.3120.6050.6900.0000.2880.3820.2820.2910.1320.0090.2520.1530.4710.1700.3060.2370.2050.1190.0000.2590.4530.6010.4880.0000.2650.3510.3780.0940.2800.1130.1530.1880.1270.0000.1620.4570.3440.3500.3490.3170.1770.5860.9960.9950.9950.9980.9970.598
vote_finalpassage0.0500.0290.1410.7270.1090.1450.1880.0600.1381.0000.0320.4600.1030.1890.2410.0000.1070.0190.1880.3250.4230.0000.0000.0000.0300.0000.1370.5670.5480.0740.0650.1760.1440.4460.2530.0000.0600.0820.0620.0890.1390.0000.0000.0000.0000.0000.0320.0310.2330.2450.2640.1480.0240.4350.1380.1370.1370.1260.1140.106
vote_numproposals0.9990.1860.1110.7050.0080.1680.3050.0920.1460.0321.0000.5590.0001.0000.0000.0000.0080.0430.0480.0310.0330.0000.0000.0000.0000.0000.1690.0610.0410.2471.0000.4811.0001.0000.0000.0000.0000.0270.0610.0120.0350.0000.0000.0000.0001.0000.3270.0780.0810.1460.1460.1440.4030.0370.2600.2600.2600.2640.1110.064
sponsor10.4560.3410.2660.4510.2030.2240.2630.2480.2420.4600.5591.0000.4160.4020.2900.9970.9740.8180.8450.6280.9850.0000.5310.0000.3360.9320.7450.6570.6950.5720.9880.4720.5260.5970.0710.0000.6150.3910.5670.3670.4010.7970.0000.4560.6700.5120.0720.3630.2790.3210.3140.2890.5030.3380.2200.2210.2210.2190.2180.239
sponsor20.3480.6220.4750.2030.0720.1110.0520.4090.3120.1030.0000.4161.0000.6350.5511.0000.8140.9470.7030.9420.6980.0000.6460.4690.4060.9961.0000.4440.4240.3741.0000.4290.8440.6150.6830.0000.0000.3650.1480.1690.2550.3190.4140.2830.9960.0910.0000.1540.2070.1550.1120.1340.1490.2510.5340.5340.5340.5170.5490.549
sponsor30.2910.6830.5480.1630.0340.2730.5250.3960.6050.1891.0000.4020.6351.0000.5771.0000.6080.7880.8220.6200.4030.9690.8810.8300.9041.0001.0000.6780.7610.2061.0000.1930.6081.0001.0000.0001.0000.6010.3530.2710.2311.0000.7730.5131.0000.0000.0000.5360.5010.4410.4490.5360.2430.5900.6670.6670.6670.6380.6460.675
sponsor40.3230.9480.7910.1950.2610.3090.4540.6370.6900.2410.0000.2900.5510.5771.0001.0000.0000.4320.3380.9700.4511.0000.8810.9920.9921.0001.0000.1460.2280.118NaN0.4880.4161.0000.4941.0001.0000.3780.6320.5550.4461.0001.0001.0001.000NaN1.0000.2780.2340.3420.6110.6820.0000.5850.8590.8590.8590.9400.8520.653
sponsor_kpd0.0000.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.9971.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0301.0000.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0370.0260.0000.0000.0240.0000.0000.0000.0000.0000.051
sponsor_leftpds0.0000.2360.2980.0900.1970.2580.4240.1940.2880.1070.0080.9740.8140.6080.0000.0001.0000.0960.1880.1400.1690.0000.0080.0000.0410.0000.0940.1700.1850.1170.0000.6420.6141.0000.0000.0000.5930.0850.1600.0710.1100.0000.0000.0000.0000.1020.0000.0000.0650.1210.0810.0220.0000.1540.3060.3060.3060.3010.2900.161
sponsor_greens0.0880.0840.4070.0840.1370.2040.1480.2810.3820.0190.0430.8180.9470.7880.4320.0000.0961.0000.0360.2330.2760.0000.0430.0000.0860.0000.1750.0340.0710.0200.0110.4620.7860.0000.0000.0000.0960.4670.1530.0990.1720.0000.0000.0250.0000.1070.0300.1290.1670.1250.1250.1370.0530.2350.4340.4350.4350.4130.3990.350
sponsor_spd0.0810.1840.2960.1990.0240.2250.2730.1710.2820.1880.0480.8450.7030.8220.3380.0000.1880.0361.0000.1100.0400.0000.0000.0000.0950.0290.2680.2540.2060.1450.1460.5140.5420.5230.1290.0000.1470.0870.4710.0620.1030.0000.0000.0230.0130.1140.0240.1340.1460.1320.1320.1390.0420.2650.3290.3290.3290.2880.3160.272
sponsor_fdp0.0740.2220.3340.3870.0700.1590.2140.2590.2910.3250.0310.6280.9420.6200.9700.0000.1400.2330.1101.0000.4360.0280.0290.0000.2500.0120.2000.5160.5130.0730.0780.3590.6660.6650.9980.0000.1130.0450.0380.3360.2040.0050.0000.0000.0000.1820.0150.1090.1280.2350.1990.1740.0000.2000.3690.3690.3690.3590.3880.335
sponsor_cducsu0.0480.0720.1560.4570.0900.1870.1560.0970.1320.4230.0330.9850.6980.4030.4510.0000.1690.2760.0400.4361.0000.0210.0060.0490.2340.0000.2290.5810.6220.0400.1480.4040.5430.5620.2540.0000.1160.0560.0530.1340.3840.0160.0230.0000.0000.1070.0000.0960.1330.2050.2030.1200.0000.2570.1660.1660.1660.1570.1870.099
sponsor_dsu0.0000.0150.0680.0000.0001.0001.0000.0500.0090.0000.0000.0000.0000.9691.0000.0000.0000.0000.0000.0280.0211.0000.0000.0000.0000.0000.0000.0210.0170.0001.0000.0000.0000.4881.0000.0000.0000.0520.0000.0710.0440.0000.0000.0000.0001.0000.0000.0000.0000.0040.0460.2140.0000.0000.4580.4880.4881.0000.4900.496
sponsor_gbbhe0.0000.1190.2800.0000.0220.1090.1830.1830.2520.0000.0000.5310.6460.8810.8810.0000.0080.0430.0000.0290.0060.0001.0000.0380.2050.0000.0260.0000.0200.0001.0000.4290.9841.0001.0000.0000.0000.0270.0770.0000.0000.4620.0000.0000.0001.0000.0000.0710.0840.0740.0740.0850.0000.2250.4210.4210.4210.4780.3550.431
sponsor_dafvp0.0000.0360.0890.0000.0000.0001.0000.1010.1530.0000.0000.0000.4690.8300.9920.0000.0000.0000.0000.0000.0490.0000.0381.0000.1320.0000.0000.0220.0170.0001.0000.0170.6790.6521.0000.0000.0000.0000.0000.0000.0500.0000.3730.1900.0001.0000.0000.0000.0690.0590.0450.0130.0000.0780.1810.1830.1831.0000.1890.203
sponsor_dp0.0450.2080.4480.0980.0700.1450.3950.1790.4710.0300.0000.3360.4060.9040.9920.0000.0410.0860.0950.2500.2340.0000.2050.1321.0000.0000.0610.1510.2110.0001.0000.2910.4070.4971.0000.0490.0320.0610.0680.0330.1020.0000.0750.2040.0001.0000.0000.1300.1380.1870.1610.1380.0340.3900.4910.4910.4910.5230.5250.482
sponsor_fu0.0000.0580.1400.0000.0000.0000.0000.0930.1700.0000.0000.9320.9961.0001.0000.0000.0000.0000.0290.0120.0000.0000.0000.0000.0001.0000.0000.0200.0000.0211.0000.7870.4941.0001.0000.0000.0000.0000.0000.0000.0410.0000.0000.0000.7111.0000.0000.0270.0380.0300.0400.1040.0000.1160.2130.2130.2130.2210.2180.216
sponsor_noparty0.2590.1660.3420.2090.1390.2640.2960.2390.3060.1370.1690.7451.0001.0001.0000.0000.0940.1750.2680.2000.2290.0000.0260.0000.0610.0001.0000.2270.2470.0991.0000.2310.0000.4881.0000.0000.0000.0400.1400.0380.0320.0000.0000.0000.0001.0000.0000.2160.2160.2200.2180.2110.1640.2050.3500.3500.3500.3480.3530.153
sponsor_govall0.0540.0000.2150.6240.1300.1430.2740.1140.2370.5670.0610.6570.4440.6780.1460.0000.1700.0340.2540.5160.5810.0210.0000.0220.1510.0200.2271.0000.9220.1480.1460.2060.2480.4360.1940.0000.1080.0440.0390.1290.1670.0160.0000.0000.0000.1140.0000.0000.2980.3110.3070.0870.0580.3050.2530.2530.2530.2140.2380.224
sponsor_govone0.0630.0620.1930.6030.1030.1760.2880.1130.2050.5480.0410.6950.4240.7610.2280.0000.1850.0710.2060.5130.6220.0170.0200.0170.2110.0000.2470.9221.0000.0900.1480.2430.1720.3880.1940.0000.1220.0100.0330.1510.2050.0010.0170.0360.0000.1070.0000.0470.2670.3850.3650.0980.0420.2890.2390.2390.2390.2110.2410.190
sponsor_mps0.2850.1130.1810.1800.0850.1860.4270.3150.1190.0740.2470.5720.3740.2060.1180.0300.1170.0200.1450.0730.0400.0000.0000.0000.0000.0210.0990.1480.0901.0000.0000.2700.3141.0000.0470.0000.0540.0750.0960.0410.0420.0000.0000.0000.0390.0000.0830.1000.1110.1190.1680.1670.3370.0730.3100.3090.3090.3200.3070.146
sponsor_afd0.0001.0001.0000.0420.2600.1281.0001.0000.0000.0651.0000.9881.0001.000NaN1.0000.0000.0110.1460.0780.1481.0001.0001.0001.0001.0001.0000.1460.1480.0001.0000.4331.0000.0000.0001.0000.0000.0110.1461.0000.0001.0001.0001.0001.0000.4331.0000.1220.1720.1720.2210.1220.0000.0231.0001.0001.0001.0001.0001.000
request10.4410.4480.3320.3220.1850.2030.1710.3470.2590.1760.4810.4720.4290.1930.4880.0000.6420.4620.5140.3590.4040.0000.4290.0170.2910.7870.2310.2060.2430.2700.4331.0000.5150.7440.4190.9970.9320.8150.9150.6120.9870.8420.0170.6830.9970.9920.9970.9970.7450.8030.8280.7710.3170.3260.3120.3120.3120.3120.2890.275
request20.5580.5450.4310.1950.0990.1790.0000.3970.4530.1441.0000.5260.8440.6080.4161.0000.6140.7860.5420.6660.5430.0000.9840.6790.4070.4940.0000.2480.1720.3141.0000.5151.0000.7130.6261.0000.4850.8990.7370.9090.8310.9840.9840.6170.4941.0001.0001.0000.3500.3930.4790.5100.3450.3200.6010.6010.6010.5350.5880.611
request31.0000.3520.5020.3720.3420.3030.7070.6440.6010.4461.0000.5970.6151.0001.0001.0001.0000.0000.5230.6650.5620.4881.0000.6520.4971.0000.4880.4360.3881.0000.0000.7440.7131.0001.0001.0000.3430.5970.8250.7800.7121.0000.8940.8941.0000.0001.0001.0000.8940.8940.0000.8130.2880.4580.6590.6590.6590.6790.6660.653
request40.0000.9980.9980.1650.0680.0001.0000.9980.4880.2530.0000.0710.6831.0000.4941.0000.0000.0000.1290.9980.2541.0001.0001.0001.0001.0001.0000.1940.1940.0470.0000.4190.6261.0001.0001.0000.9980.9980.5920.9980.6131.0001.0001.0001.0000.0001.0000.1720.1180.1200.1720.9180.0000.1220.9980.9980.9980.9981.0000.998
request_kpd0.0000.0000.0000.0110.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0490.0000.0000.0000.0000.0001.0000.9971.0001.0001.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0240.0000.0000.0000.0000.0000.051
request_leftpds0.0000.2050.3000.0630.2480.3050.3670.2260.2650.0600.0000.6150.0001.0001.0000.0000.5930.0960.1470.1130.1160.0000.0000.0000.0320.0000.0000.1080.1220.0540.0000.9320.4850.3430.9980.0001.0000.0620.1330.0440.0830.0000.0000.0000.0000.1280.0000.1650.1900.2350.2560.1770.0200.1510.3400.3400.3400.3380.3310.158
request_greens0.0160.0000.3420.0710.0950.1880.2700.2170.3510.0820.0270.3910.3650.6010.3780.0000.0850.4670.0870.0450.0560.0520.0270.0000.0610.0000.0400.0440.0100.0750.0110.8150.8990.5970.9980.0000.0621.0000.0270.0550.1230.0000.0000.0040.0000.1450.0130.2620.2830.2770.2730.2780.0430.1770.3690.3700.3700.3570.3230.250
request_spd0.0530.3050.3820.0750.0000.2420.3060.2450.3780.0620.0610.5670.1480.3530.6320.0000.1600.1530.4710.0380.0530.0000.0770.0000.0680.0000.1400.0390.0330.0960.1460.9150.7370.8250.5920.0000.1330.0271.0000.0880.1140.0000.0000.0400.0000.3200.0460.4580.4680.4630.4740.4870.0710.3220.4100.4120.4120.3850.4160.331
request_fdp0.0860.0700.1640.0940.0400.1100.0000.1640.0940.0890.0120.3670.1690.2710.5550.0000.0710.0990.0620.3360.1340.0710.0000.0000.0330.0000.0380.1290.1510.0411.0000.6120.9090.7800.9980.0000.0440.0550.0881.0000.2260.0000.0000.1050.0001.0000.0000.2020.3730.3160.3170.2880.0140.0910.1990.2000.2000.1890.2220.120
request_cducsu0.0660.1590.2890.1350.0000.1200.1640.1330.2800.1390.0350.4010.2550.2310.4460.0000.1100.1720.1030.2040.3840.0440.0000.0500.1020.0410.0320.1670.2050.0420.0000.9870.8310.7120.6130.0000.0830.1230.1140.2261.0000.0000.0500.0590.0000.1280.0220.3020.4260.5520.5110.3550.0400.1980.3320.3340.3340.3340.3430.256
request_gbbhe0.0000.0540.1290.0220.0000.0001.0000.0870.1130.0000.0000.7970.3191.0001.0000.0000.0000.0000.0000.0050.0160.0000.4620.0000.0000.0000.0000.0160.0010.0001.0000.8420.9841.0001.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0001.0000.0000.0230.0340.0390.0600.1110.0000.1080.2160.2160.2160.2560.1160.233
request_dafvp0.0000.0360.0890.0000.0280.0451.0000.1010.1530.0000.0000.0000.4140.7731.0000.0000.0000.0000.0000.0000.0230.0000.0000.3730.0750.0000.0000.0000.0170.0001.0000.0170.9840.8941.0000.0000.0000.0000.0000.0000.0500.0001.0000.4500.0001.0000.0000.0000.1120.0590.0720.0700.0000.0780.1810.1830.1831.0000.1890.203
request_dp0.0000.0840.1710.0000.0580.1130.0000.0700.1880.0000.0000.4560.2830.5131.0000.0000.0000.0250.0230.0000.0000.0000.0000.1900.2040.0000.0000.0000.0360.0001.0000.6830.6170.8941.0000.0000.0000.0040.0400.1050.0590.0000.4501.0000.0001.0000.0000.0470.1330.1490.1470.0830.0000.1620.1880.1890.1890.1950.1930.204
request_fu0.0000.0430.1040.0000.0000.0000.0000.0660.1270.0000.0000.6700.9961.0001.0000.0000.0000.0000.0130.0000.0000.0000.0000.0000.0000.7110.0000.0000.0000.0391.0000.9970.4941.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.0000.0000.0110.0240.0360.0180.1320.0000.0890.1550.1550.1550.1650.1630.166
request_afd0.0001.0001.0000.0000.2020.2761.0001.0000.0000.0001.0000.5120.0910.000NaN1.0000.1020.1070.1140.1820.1071.0001.0001.0001.0001.0001.0000.1140.1070.0000.4330.9921.0000.0000.0001.0000.1280.1450.3201.0000.1281.0001.0001.0001.0001.0001.0000.3990.4760.4760.5600.3990.0000.0311.0001.0001.0001.0001.0001.000
request_noparty0.6050.2470.1680.3140.0420.1930.0000.2170.1620.0320.3270.0720.0000.0001.0000.0000.0000.0300.0240.0150.0000.0000.0000.0000.0000.0000.0000.0000.0000.0831.0000.9971.0001.0001.0000.0000.0000.0130.0460.0000.0220.0000.0000.0000.0001.0001.0000.0530.0650.0370.0600.2520.2400.0180.3810.3810.3810.3950.1380.119
request_unknown0.1160.3140.4040.1050.0610.3710.3280.2800.4570.0310.0780.3630.1540.5360.2780.0000.0000.1290.1340.1090.0960.0000.0710.0000.1300.0270.2160.0000.0470.1000.1220.9971.0001.0000.1720.0000.1650.2620.4580.2020.3020.0230.0000.0470.0110.3990.0531.0001.0000.9910.9910.9910.1480.3520.4410.4410.4410.4110.4350.314
request_gov0.0780.2470.3200.1460.0600.2640.2390.2280.3440.2330.0810.2790.2070.5010.2340.0000.0650.1670.1460.1280.1330.0000.0840.0690.1380.0380.2160.2980.2670.1110.1720.7450.3500.8940.1180.0000.1900.2830.4680.3730.4260.0340.1120.1330.0240.4760.0651.0001.0000.8090.7730.7010.1500.2710.3500.3500.3500.3310.3440.263
request_govpart0.1120.2200.3180.1770.0520.2670.2540.2100.3500.2450.1460.3210.1550.4410.3420.0370.1210.1250.1320.2350.2050.0040.0740.0590.1870.0300.2200.3110.3850.1190.1720.8030.3930.8940.1200.0000.2350.2770.4630.3160.5520.0390.0590.1490.0360.4760.0370.9910.8091.0000.9100.7100.1850.2610.3630.3640.3640.3320.3530.257
request_oppo0.1030.2170.3140.1930.0510.2670.2400.2510.3490.2640.1460.3140.1120.4490.6110.0260.0810.1250.1320.1990.2030.0460.0740.0450.1610.0400.2180.3070.3650.1680.2210.8280.4790.0000.1720.0000.2560.2730.4740.3170.5110.0600.0720.1470.0180.5600.0600.9910.7730.9101.0000.7310.1880.2560.3810.3820.3820.3580.3800.254
request_govoppo0.1010.2400.3070.1180.0610.2560.2430.2760.3170.1480.1440.2890.1340.5360.6820.0000.0220.1370.1390.1740.1200.2140.0850.0130.1380.1040.2110.0870.0980.1670.1220.7710.5100.8130.9180.0000.1770.2780.4870.2880.3550.1110.0700.0830.1320.3990.2520.9910.7010.7100.7311.0000.2080.2640.3730.3740.3740.3480.3840.253
free_vote0.4130.0880.1690.3030.1350.2170.4440.1370.1770.0240.4030.5030.1490.2430.0000.0000.0000.0530.0420.0000.0000.0000.0000.0000.0340.0000.1640.0580.0420.3370.0000.3170.3450.2880.0000.0000.0200.0430.0710.0140.0400.0000.0000.0000.0000.0000.2400.1480.1500.1850.1880.2081.0000.0970.2130.2120.2120.2180.1610.087
bundesrat0.1170.3970.6020.3990.2430.3140.4620.2460.5860.4350.0370.3380.2510.5900.5850.0240.1540.2350.2650.2000.2570.0000.2250.0780.3900.1160.2050.3050.2890.0730.0230.3260.3200.4580.1220.0240.1510.1770.3220.0910.1980.1080.0780.1620.0890.0310.0180.3520.2710.2610.2560.2640.0971.0000.6070.6070.6070.6080.5990.517
cabinet0.5000.7020.9950.2130.2140.2480.4000.9960.9960.1380.2600.2200.5340.6670.8590.0000.3060.4340.3290.3690.1660.4580.4210.1810.4910.2130.3500.2530.2390.3101.0000.3120.6010.6590.9980.0000.3400.3690.4100.1990.3320.2160.1810.1880.1551.0000.3810.4410.3500.3630.3810.3730.2130.6071.0000.9960.9960.9990.9970.989
cab_start0.5000.7020.9960.2120.2190.2480.4000.9940.9950.1370.2600.2210.5340.6670.8590.0000.3060.4350.3290.3690.1660.4880.4210.1830.4910.2130.3500.2530.2390.3091.0000.3120.6010.6590.9980.0000.3400.3700.4120.2000.3340.2160.1830.1890.1551.0000.3810.4410.3500.3640.3820.3740.2120.6070.9961.0001.0001.0001.0000.996
cab_end0.5000.7020.9960.2120.2190.2480.4000.9940.9950.1370.2600.2210.5340.6670.8590.0000.3060.4350.3290.3690.1660.4880.4210.1830.4910.2130.3500.2530.2390.3091.0000.3120.6010.6590.9980.0000.3400.3700.4120.2000.3340.2160.1830.1890.1551.0000.3810.4410.3500.3640.3820.3740.2120.6070.9961.0001.0001.0001.0000.996
elecper_start0.5030.7040.9980.2070.2100.2350.4000.9960.9980.1260.2640.2190.5170.6380.9400.0000.3010.4130.2880.3590.1571.0000.4781.0000.5230.2210.3480.2140.2110.3201.0000.3120.5350.6790.9980.0000.3380.3570.3850.1890.3340.2561.0000.1950.1651.0000.3950.4110.3310.3320.3580.3480.2180.6080.9991.0001.0001.0001.0000.997
elecper_end0.4880.7030.9980.1340.2040.2480.3700.9950.9970.1140.1110.2180.5490.6460.8520.0000.2900.3990.3160.3880.1870.4900.3550.1890.5250.2180.3530.2380.2410.3071.0000.2890.5880.6661.0000.0000.3310.3230.4160.2220.3430.1160.1890.1930.1631.0000.1380.4350.3440.3530.3800.3840.1610.5990.9971.0001.0001.0001.0000.997
cab_parties0.2500.5260.6710.1030.1120.1590.3550.5640.5980.1060.0640.2390.5490.6750.6530.0510.1610.3500.2720.3350.0990.4960.4310.2030.4820.2160.1530.2240.1900.1461.0000.2750.6110.6530.9980.0510.1580.2500.3310.1200.2560.2330.2030.2040.1661.0000.1190.3140.2630.2570.2540.2530.0870.5170.9890.9960.9960.9970.9971.000

Missing values

2023-12-03T11:22:37.371419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-03T11:22:37.759398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-03T11:22:38.318800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

vote_idvote_id2vote_id_elecperelecpersourcevote_titlevote_typevote_finalpassagevote_numproposalspolicy1policy2policy3sponsor1sponsor2sponsor3sponsor4sponsor_kpdsponsor_leftpdssponsor_greenssponsor_spdsponsor_fdpsponsor_cducsusponsor_dsusponsor_gbbhesponsor_dafvpsponsor_dpsponsor_fusponsor_nopartysponsor_govallsponsor_govonesponsor_mpssponsor_afdrequest1request2request3request4request_kpdrequest_leftpdsrequest_greensrequest_spdrequest_fdprequest_cducsurequest_gbbherequest_dafvprequest_dprequest_furequest_afdrequest_nopartyrequest_unknownrequest_govrequest_govpartrequest_opporequest_govoppofree_votebundesratgestacabid_parlgovcabid_erddacabinetcab_startcab_endelecper_startelecper_endcab_partiesvote_date
010011001.01101/069/2520Entwurf eines Gesetzes ��ber den Beitritt der Bundesrepublik Deutschland zum Europarat (Drucksache Nr. 984)10.01019NaNNaNCDU/CSUFDPDPNaN0.00.00.00.01.01.00.00.00.01.00.00.01.01.00.0NaNCDU/CSUNaNNaNNaN0.00.00.00.00.01.00.00.00.00.0NaN0.00.00.01.00.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1950-06-15
110021002.02101/076/2738Handschriftlicher ��nderungsantrag der Abgeordneten Pelster und Genossen zu ��1 Abs. 1 des Entwurfs eines Richterwahlgesetzes (Drucksache Nr. 1088)5.00012NaNNaNCDU/CSUNaNNaNNaN0.00.00.00.00.01.00.00.00.00.00.00.00.01.01.0NaNCDU/CSUNaNNaNNaN0.00.00.00.00.01.00.00.00.00.0NaN0.00.00.01.00.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1950-07-14
210031003.03101/079/2923Artikel I Ziffer 2 des Entwurfs eines Gesetzes zur ��nderung des Umsatzsteuergesetzes (Drucksachen Nr. 1123 und 1215)10.0001NaNNaNSPDNaNNaNNaN0.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0NaNFDPNaNNaNNaN0.00.00.00.01.00.00.00.00.00.0NaN0.00.00.01.00.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1950-07-26
310041004.04101/150/5989Antrag der Fraktion der Deutschen Partei betreffend Einsetzung eines Untersuchungsausschusses (Drucksachen Nr. 2234)1.00020NaNNaNDPNaNNaNNaN0.00.00.00.00.00.00.00.00.01.00.00.00.01.00.0NaNCDU/CSUNaNNaNNaN0.00.00.00.00.01.00.00.00.00.0NaN0.00.00.01.00.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1951-06-08
410051005.05101/183/7787Artikel I des Entwurfs eines Gesetzes betreffend den Vertrag ��ber die Gr��ndung der Europ��ischen Gemeinschaft f��r Kohle und Stahl (Drucksachen Nr. 2401)10.0001819.01.0CDU/CSUFDPDPNaN0.00.00.00.01.01.00.00.00.01.00.00.01.01.00.0NaNKPDNaNNaNNaN1.00.00.00.00.00.00.00.00.00.0NaN0.00.00.00.01.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-10
510061006.06101/183/7787��nderungsantrag der Fraktion der SPD zur zweiten Beratung des Entwurfs eines Gesetzes betreffend den Vertrag ��ber die Gr��ndung der Europ��ischen Gemeinschaft f��r Kohle und Stahl (Umdruck Nr. 407)6.0001819.01.0SPDNaNNaNNaN0.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0NaNSPDNaNNaNNaN0.00.00.01.00.00.00.00.00.00.0NaN0.00.00.00.01.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-10
610071007.07101/184/7833Artikel I des Entwurfs eines Gesetzes betreffend den Vertrag ��ber die Gr��ndung der Europ��ischen Gemeinschaft f��r Kohle und Stahl vom 18. April 1951 (Nr. 2401 der Drucksachen)10.0001819.01.0CDU/CSUFDPDPNaN0.00.00.00.01.01.00.00.00.01.00.00.01.01.00.0NaNCDU/CSUFDPDPNaN0.00.00.00.01.01.00.00.01.00.0NaN0.00.01.01.00.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-11
710081008.08101/184/7833��nderungsantrag der Fraktion der SPD zur dritten Beratung des Entwurfs eines Gesetzes betreffend den Vertrag ��ber die Gr��ndung der Europ��ischen Gemeinschaft f��r Kohle und Stahl (Umdruck Nr. 413)6.0001819.01.0SPDNaNNaNNaN0.00.00.01.00.00.00.00.00.00.00.00.00.00.00.0NaNSPDNaNNaNNaN0.00.00.01.00.00.00.00.00.00.0NaN0.00.00.00.01.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-11
810091009.09101/185/7905��1 des Entwurfs eines Gesetzes ��ber die Aussetzung des Vollzugs von Bestimmungen des Zweiten Neugliederungsgesetzes (Drucksachen Nr. 2942)10.0002420.0NaNCDU/CSUNaNNaNNaN0.00.00.00.00.01.00.00.00.00.00.00.00.01.00.0NaNCDU/CSUNaNNaNNaN0.00.00.00.00.01.00.00.00.00.0NaN0.00.00.01.00.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-16
910101010.010101/187/7977Zweite Beratung des von den Fraktionen der CDU/CSU, FDP und DP eingebrachten Entwurfs eines Gesetzes ��ber die Errichtung einer Bundesanstalt f��r Arbeitsvermittlung und Arbeitslosenversicherung: ��ber den ��nderungsantrag der Abgeordneten Atzenroth und Genossen zu ��2 Abs. 2 (Umdruck Nr. 421 Ziffer 1)5.00015.0NaNCDU/CSUFDPDPNaN0.00.00.00.01.01.00.00.00.01.00.00.01.01.00.0NaNSPDNaNNaNNaN0.00.00.01.00.00.00.00.00.00.0NaN0.00.00.00.01.00.0099NaN147.0601.0Adenauer I1949-09-151953-09-061949-08-141953-09-06CDU/CSU, FDP, DP1952-01-23
vote_idvote_id2vote_id_elecperelecpersourcevote_titlevote_typevote_finalpassagevote_numproposalspolicy1policy2policy3sponsor1sponsor2sponsor3sponsor4sponsor_kpdsponsor_leftpdssponsor_greenssponsor_spdsponsor_fdpsponsor_cducsusponsor_dsusponsor_gbbhesponsor_dafvpsponsor_dpsponsor_fusponsor_nopartysponsor_govallsponsor_govonesponsor_mpssponsor_afdrequest1request2request3request4request_kpdrequest_leftpdsrequest_greensrequest_spdrequest_fdprequest_cducsurequest_gbbherequest_dafvprequest_dprequest_furequest_afdrequest_nopartyrequest_unknownrequest_govrequest_govpartrequest_opporequest_govoppofree_votebundesratgestacabid_parlgovcabid_erddacabinetcab_startcab_endelecper_startelecper_endcab_partiesvote_date
24211923519235.02351919/234/30318Antrag der Fraktionen der CDU/CSU und SPD Feststellung des Fortbestehens der epidemischen Lage von nationaler Tragweite (Drs.19/30398)1.000320.0NaNCDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.0FDPNaNNaNNaN0.00.00.01.00.00.00.00.00.00.00.00.00.00.00.01.00.000NaN1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-11
24221923619236.02361919/236/30678Gesetzentwurf der Bundesregierung Entwurf eines Dritten Gesetzes zur ��nderung des Bundesnaturschutzgesetzes (Drs.19/28182 und 19/30713)10.0007NaNNaNCDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.0AfDNaNNaNNaN0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.001N0311528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24231923719237.02371919/236/30656Gesetzentwurf der Bundesregierung Entwurf eines Ersten Gesetzes zur ��nderung des Bundes-Klimaschutzgesetzes (Drs.19/30230 und 19/30949)10.0007NaNNaNCDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.0AfDNaNNaNNaN0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.001N0301528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24241923819238.02381919/236/30731Beschlussempfehlung des Ausw��rtigen Ausschusses (3. Ausschuss) zu dem Antrag der Bundesregierung Fortsetzung der Beteiligung bewaffneter deutscher Streitkr��fte an der \United Nations Interium Force in Lebanon\" (UNIFIL) (Drs.19/29626 und 19/30630)"8.00016NaNNaNCommitteeNaNNaNNaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0CDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.00.01.01.00.00.000NaN1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24251923919239.02391919/236/30731Beschlussempfehlung des Ausw��rtigen Ausschusses (3. Ausschuss) zu dem Antrag der Bundesregierung Fortsetzung der Beteiligung bewaffneter deutscher Streitkr��fte an der internationalen Sicherheitspr��senz in Kosovo (KFOR) (Drs.19/29625 und 19/30628)8.00016NaNNaNCommitteeNaNNaNNaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0CDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.00.01.01.00.00.000NaN1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24261924019240.02401919/236/30701Gesetzentwurf der Bundesregierung Entwurf eines Gesetzes zur Vereinheitlichung des Stiftungsrechts in der Ausschussfassung hier: Artikel 9 (Infektionsschutzgesetz) und Artikel 10 (Einschr��nkung von Grundrechten) (Drs.19/28173, 19/30938 und 19/31118)10.00099NaNNaNCDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.0AfDNaNNaNNaN0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.002C2091528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24271924119241.02411919/236/30710Antrag der Abgeordneten Stephan Brandner, Dr. Heiko He��enkemper, Nicole H��chst, weiterer Abgeordneter und der Fraktion der AfD Keine Verwendung der sogenannten gendergerechten Sprache durch die Bundesregierung (Drs.19/30964)1.000220.0NaNAfDNaNNaNNaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.01.0AfDNaNNaNNaN0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.000NaN1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-06-24
24281924219242.02421919/238/31034Antrag der Bundesregierung Einsatz bewaffneter deutscher Streitkr��fte zur milit��rischen Evakuierung aus Afghanistan Drs. 19/32022 (Drs.19/32022)1.00016NaNNaNCDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.0unknownNaNNaNNaN0.00.00.00.00.00.00.00.00.00.00.00.01.099.099.099.099.000NaN1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-08-25
24291924319243.02431919/238/31076Antrag der Fraktionen der CDU/CSU und SPD Feststellung des Fortbestehens der epidemischen Lage von nationaler Tragweite Drs. 19/32091 (Drs. 19/32091)1.000320.0NaNCDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.0AfDNaNNaNNaN0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.000NaN1528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-08-25
24301924419244.02441919/239/31171Zweite und dritte Beratung des von den Fraktionen der CDU/CSU und SPD eingebrachten Entwurfs eines Gesetzes zur Errichtung eines Sonderverm��gens ���Aufbauhilfe 2021��� und zur vor��bergehenden Aussetzung der Insolvenzantragspflicht wegen Starkregenf��llen und Hochwassern im Juli 2021 sowie zur ��nderung weiterer Gesetze (Aufbauhilfegesetz 2021 ��� AufbhG 2021) (Drs.19/32039, 19/32275)10.01020NaNNaNCDU/CSUSPDNaNNaN0.00.00.01.00.01.00.00.00.00.00.00.01.01.00.00.0AfDNaNNaNNaN0.00.00.00.00.00.00.00.00.00.01.00.00.00.00.01.00.002D1151528.0NaNMerkel IV2018-03-142021-10-262017-10-272021-10-26CDU/CSU, SPD2021-09-07